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Joint multi-mode cooperative classification algorithm for hyperspectral images J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Xiaowei Ji; Ying Cui; Long Teng
Hyperspectral image (HSI) classification is a challenging problem due to the high dimensional features, high intra-class variance, and limited prior information, and the classification is the basis for HSI applications. Active learning (AL) and semisupervised learning (SSL) are two promising approaches in the HSI classification. In AL, the traditional entropy query-by-bagging (EQB) algorithm only pays
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Super-resolution reconstruction of single remote sensing images based on residual channel attention J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Li Gao; Hong-Mei Sun; Zhe Cui; Yan-Bin Du; Hai-Bin Sun; Rui-Sheng Jia
The existing methods of remote sensing image super-resolution reconstruction based on deep learning have some problems, such as insufficient feature extraction abilities, blurred image edges, and difficult model training. To solve these problems, a super-resolution reconstruction method combining residual channel attention (CA) is proposed. Based on the framework of generative adversarial networks
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Clutter suppression methods based on reduced-dimension transformation for airborne passive radar with impure reference signals J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Yaqi Deng; Saiwen Zhang; Qiuxiang Zhu; Lincheng Zhang; Wenguo Li
For an airborne passive radar with impure reference signals, the clutter caused by multipath (MP) signals involved in the reference channel (MP clutter) corrupts the space–time adaptive processing performances. To eliminate the influence of the MP clutter, two clutter suppression methods based on reduced-dimension (RD) transformation are proposed herein. RD transformation is exploited to reduce the
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High-resolution synthetic aperture radar image classification using multi-scale anisotropic convolutional sparse coding J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Yan Wu; Wenkai Liang; Yice Cao; Ming Li; Xin Hu
The classification of high-resolution (HR) synthetic aperture radar (SAR) image is of great significance to SAR scene interpretation and understanding. However, the HR SAR image contains rich ground information and complex spatial structural features. The extraction of effective distinguishing features under the influence of coherent speckle is still a challenging task. A multi-scale anisotropic convolution
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Characterization and monitoring of GOES-16 ABI stray light and comparison with Himawari-8 AHI and GOES-17 ABI J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Xi Shao; Xiangqian Wu; Fangfang Yu; Changyong Cao
The Advanced Baseline Imager (ABI) aboard Geostationary Operational Environmental Satellite (GOES)-16 and -17 satellites represent the next-generation geostationary multispectral imaging instrument. Since GOES-16 ABI imagery data became available, stray light was observed in ABI visible, near-infrared (VNIR), and 3.9 μm, i.e., CH07, channels. A stray-light characterization scheme was developed to quantitatively
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Application of Taguchi method to improve land use land cover classification using PCA-DWT-based SAR-multispectral image fusion J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Samadhan C. Kulkarni; Priti P. Rege
The fusion of multispectral and synthetic aperture radar (SAR) images is of vital importance in many remote sensing applications. Spectral distortion and trade-off between the spatial and spectral quality of the fused image are significant issues in SAR-multispectral image fusion. Our study attempts to improve the performance of SAR-multispectral image fusion concerning these two issues. The primary
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Automatic detection of surface-water bodies from Sentinel-1 images for effective mosquito larvae control J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Georgios Ovakoglou; Ines Cherif; Thomas K. Alexandridis; Xanthoula-Eirini Pantazi; Afroditi-Alexandra Tamouridou; Dimitrios Moshou; Xanthi Tseni; Iason Raptis; Stella Kalaitzopoulou; Spiros Mourelatos
Surface-water body maps are imperative for effective mosquito larvae control. This study aims to select a method for the automatic and regular mapping of surface-water bodies in rice fields and wetlands using Sentinel-1 synthetic aperture radar data. Four methods were adapted and developed for automated application: the Otsu valley-emphasis algorithm, a classification method based on the textural feature
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Use of noise reduction filters on stereo images for improving the accuracy and quality of the digital elevation model J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Litesh Bopche; Priti P. Rege
Recently, there has been increasing attention to the digital elevation model (DEM) because of its ability to learn the Earth’s surface’s topography. Freely accessible DEMs such as the Cartosat-1, shuttle radar topography mission (SRTM), light detection and ranging, and advanced spaceborne thermal emission and reflection radiometer DEM, contain large vertical errors. The errors are aggravated over multifaceted
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Verifiable XOR-based visual secret sharing scheme for hyperspectral images J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Oruganti Sai Srujana; Nikhil C. Mhala; Alwyn R. Pais
Hyperspectral images (HSIs) are the spectral images that provide spatial and spectral information. Unlike multispectral images, these images consist of 100 to 200 bands, which provide a large amount of data to identify minute details of the scene with the help of the spectral signatures. This information is valuable and should be secured while transmitting the HSI over the network. Visual cryptography
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Object classification of remote sensing images based on optimized projection supervised discrete hashing J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Qianqian Zhang; Yazhou Liu; Quansen Sun
Recently, with the number of large-scale remote sensing (RS) images increasing, the demand for large-scale RS image object classification is growing, and many researchers are interested. Hashing, as a result of its low memory requirements and high time efficiency, has widely solved the problem of large-scale RS images. Supervised hashing methods mainly leverage RS image label information to learn hashing
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Optoelectric characteristics of laser remote sensing for measurement of ground vibration J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Bo Yan; Heping Shi; Ping Wang; Qionglin Tong; Jinshan Su
Airborne laser remote sensing is extensively used in geophysical explorations. Laser-remote-sensing studies are often undertaken to predict the availability of mineral resources in a target area. A major issue encountered in such studies concerns the acquisition of the ground-vibration information and analysis of the optoelectric characteristics of the corresponding target vibration. Our paper presents
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Land-cover classification of high-resolution remote sensing image based on multi-classifier fusion and the improved Dempster–Shafer evidence theory J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Tianjing Feng; Hairong Ma; Xinwen Cheng
The use of the single machine learning classifier for high-resolution remote sensing (RS) image classification makes it difficult to improve the accuracy of classification results. To fully utilize the advantages of different classifiers for different types of ground objects, based on the Dempster–Shafer (DS) evidence theory, we propose a multi-classifier fusion method for classification of high-resolution
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Ground target extraction using airborne streak tube imaging LiDAR J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Zhiwei Dong; Yongji Yan; Yugang Jiang; Rongwei Fan; Deying Chen
Airborne LiDAR has become a kind of indispensable measurement device in the current field of remote sensing. However, target extraction using traditional airborne LiDAR based on single-point scanning requires filtering and point cloud segmentation operations, which are complicated and time consuming. Although some researchers have studied streak tube imaging LiDAR (STIL) before, there are few reports
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Classification and status monitoring of agricultural crops in central Morocco: a synergistic combination of OBIA approach and fused Landsat-Sentinel-2 data J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Abdelaziz Htitiou; Abdelghani Boudhar; Youssef Lebrini; Hayat Lionboui; Abdelghani Chehbouni; Tarik Benabdelouahab
Crop type mapping provides essential information to control and make decisions related to agricultural practices and their regulations. To map crop types accurately, it is important to capture their phenological stages and fine spatial details, especially in a temporally and spatially heterogeneous landscape. The data availability of new generation multispectral sensors of Landsat-8 (L8) and Sentinel-2
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Mobile system: detecting buried objects by magnetic anomaly method J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-02-01 Serkan Gürkan; Mustafa Karapınar; Seydi Doğan
A mobile system has been developed to be used to detect buried objects (and/or explosives) containing ferromagnetic material by magnetic anomaly method. Detection, in this system, can be performed without broadcasting any signal to the external environment, and a visual and audible alert can be sent to the user. A sensor network containing nine fluxgate sensors has been created; one of them was used
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Determination of phosphorus status in bread wheat leaves by visible and near-infrared spectral discriminant analysis J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Pamela Aracena Santos; Erdogan Esref Hakki; Sait Gezgin; Ali Topal; Mert Dedeoglu
This study developed a quadratic discriminant analysis (QDA) model from the spectroradiometer reflections (400 to 1000 nm) and phosphorus (P) uptake in wheat under varying rates of P dosages (0, 25, and 50 ppm P) in the tillering (GS25) and heading (GS55) stages. Seventy-two experimental plants were grown under controlled greenhouse conditions. Stepwise multiple regression analysis was used to determine
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Hyperspectral image segmentation using 3D regularized subspace clustering model J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Carlos Hinojosa; Fernando Rojas; Sergio Castillo; Henry Arguello
The accurate segmentation of remotely sensed hyperspectral images has widespread attention in the Earth observation and remote sensing communities. In the past decade, most of the efforts focus on the development of different supervised methods for hyperspectral image classification. Recently, the computer vision community is developing unsupervised methods that can adapt to new conditions without
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Decorrelated unbiased converted measurement for bistatic radar tracking J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Sen Wang; Qinglong Bao
Bistatic radar target tracking is challenging due to the fact that the measurements are nonlinear functions of the Cartesian state. The converted measurement Kalman filter (CMKF) converts the raw measurement into Cartesian coordinates prior to tracking and is superior to the extended Kalman filter for certain problems. The challenges of CMKF are debiasing the converted measurement and approximating
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Fast aircraft detection method in optical remote sensing images based on deep learning J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Zhi-Feng Xu; Rui-Sheng Jia; Jin-Tao Yu; Jian-Zhi Yu; Hong-Mei Sun
In optical remote sensing images, the aircraft to be detected is very small; external environmental factors such as cloud occlusion, aircraft, and the site background are easily fused; and the interference of objects to aircraft has a great impact on the aircraft characteristics in remote sensing images. In response to the above problems, we designed a remote sensing aircraft detection method based
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Fast change detection method for remote sensing image based on method of connected area labeling and spectral clustering algorithm J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Jiu-Yuan Huo; Lin Mu
The speed of the image clustering method based on the spectral clustering algorithm is greatly affected by the size of image resolution; in many cases, the processing of high-resolution images cannot be completed precisely on time. Thus, based on the connected area labeling and superpixel spectral clustering algorithm, this paper proposes a fast change detection method for remote sensing images based
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Automatic aircraft detection in very-high-resolution satellite imagery using a YOLOv3-based process J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Yu-Ching Lin; Wei-De Chen
Aircraft detection in remote-sensing images is a fundamental task in civil and military applications. Deep learning techniques to achieve end-to-end object detection have attracted the attention of the Earth observation community. One of the primary factors behind the success of deep learning techniques is the utilized data. Several previous studies focused on designing the network infrastructure.
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SA-U-Net++: SAR marine floating raft aquaculture identification based on semantic segmentation and ISAR augmentation J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Deyi Wang; Min Han
Marine floating raft aquaculture (FRA) monitoring is vital for environment protection and mariculture management. Synthetic aperture radar (SAR) could provide high-quality remote sensing images under all weather conditions compared with the existing optical remote-sensing-based methods. Traditional SAR monitoring methods extract the pixel feature of marine FRA in single patches, which commonly leads
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Remote sensing image retrieval by integrating automated deep feature extraction and handcrafted features using curvelet transform J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Sudheer Devulapalli; Rajakumar Krishnan
Deep learning techniques have become increasingly popular for classifying large-scale image and video data. Remote sensing applications require robust search engines to retrieve similar information dependent on an example-based query instead of a tag-based query. Deep features can be extracted automatically by training raw data without having any domain-specific knowledge. However, the training time
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2020 List of Reviewers J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01
This is a list of reviewers who served the Journal of Applied Remote Sensing in 2020.
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Knowledge-aided covariance estimation and radar adaptive detection J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Ke Jin; Hongmin Zhang; Jizhou Wu; Tao Lai; Yongjun Zhao
We address the covariance matrix estimation problem for radar adaptive detection in a non-Gaussian clutter environment. We first propose an estimation method based on α log-determinant divergence, which estimates the true covariance accurately by solving the geometric mean of the sample covariance matrix (SCM). Since the estimation performance would be seriously degraded when the number of secondary
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Nonlocal weighted sparse unmixing based on global search and parallel optimization J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Yongxin Li; Wenxing Bao; Kewen Qu; Xiangfei Shen
Sparse unmixing (SU) can represent an observed image using pure spectral signatures and corresponding fractional abundance from a large spectral library and is an important technique in hyperspectral unmixing. However, the existing SU algorithms mainly exploit spatial information from a fixed neighborhood system, which is not sufficient. To solve this problem, we propose a nonlocal weighted SU algorithm
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Maneuvering platform high-squint SAR imaging method based on perturbation KT and subregion phase filtering J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Gen Li; Yanheng Ma; Xuying Xiong
The imaging parameters of high-squint synthetic aperture radar (SAR) mounted on maneuvering platforms have obvious spatial variability, which cannot be effectively solved by traditional SAR imaging algorithms and limits the focus depth. To extend the focus depth of maneuvering SAR, an imaging method is proposed based on perturbation keystone transform (KT) and subregion phase filtering (SRPF). In the
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Remote sensing for assessing vegetated roofs with a new replicable method in Paris, France J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Tanguy Louis-Lucas; Flavie Mayrand; Philippe Clergeau; Nathalie Machon
Vegetated roofs provide many ecosystem services and support urban biodiversity. While it would be interesting to study the contribution of vegetated roofs to ecological corridors, vegetated roofs are listed in no French databases. Because of their intrinsic nature as roofs, their small number, their small size, and the type of vegetation planted on them, vegetated roofs seem to be very difficult to
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Toward high-performance SPAD arrays for space-based atmosphere and ocean profiling LiDARs J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Giulia Acconcia; Andrea Giudici; John A. Smith; Ivan Labanca; Rich J. Hare; Massimo Ghioni; Ivan Rech
Space-based light detection and ranging (LiDAR) sensors have provided valuable insight into the global, vertical distribution of aerosol and cloud layers in Earth’s atmosphere, and, more recently, of the distribution of phytoplankton in the ocean. However, the photodetectors in these sensors lack the performance necessary to capture the vertical structure of cloud tops and ocean phytoplankton to a
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Hedgerow object detection in very high-resolution satellite images using convolutional neural networks J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Steve Ahlswede; Sarah Asam; Achim Röder
Hedgerows are one of the few remaining natural landscape features within European agricultural areas. To facilitate hedgerow monitoring, cost-effective and accurate mapping of hedgerows across large spatial scales is required. Current methods used for automatic hedgerow detection are overly complicated and generalize poorly to larger areas. We examine the application of transfer learning using two
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Edge-guided multispectral image fusion algorithm J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Guihui Li; Jinjiang Li; Hui Fan
Most existing multispectral fusion algorithms often suffer from spectral or spatial information distortion. Driven by this motivation, we propose an edge-guided multispectral (MS) image fusion algorithm. In particular, it combines the advantages of generative adversarial networks and improved fusion frameworks, so the merged image can better preserve the spectral information of the original multispectral
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Hyperspectral anomaly detection using a background endmember signature J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Hongwei Chang; Tao Wang; Aihua Li; Yihe Jiang
Due to lacking use of prior information, the anomaly detection results are not always satisfactory. However, with the establishment of the spectral library, it becomes possible to obtain one or more spectra of the background in the image to be detected. If we can make use of such background information that is always ignored or discarded, the detection result is very likely to be improved. Hence, we
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Destriping and evaluating FY-3D MERSI-2 data with the moment matching method based on synchronous reference image J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Kai Tang; Hongchun Zhu; Yu Cheng; Lin Zhang
Medium Resolution Spectral Imager II (MERSI-2) is a payload of the China meteorological satellite FY-3D. The sensor bands 24 (10.3 to 11.3 μm) and 25 (11.5 to 12.5 μm) images, which are most suitable for land surface temperature (LST) retrieval, have higher spatial resolution than that of similar sensors. However, bands 24/25 images with spatial resolution of 250 m (hereafter bands 24/25 250-m images)
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Two-stage ship detection in synthetic aperture radar images based on attention mechanism and extended pooling J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Chenchen Wang; Weimin Su; Hong Gu
Object detection in synthetic aperture radar (SAR) images remains a challenging problem due to the particular imaging mechanism of SAR systems. The sizes of targets are relatively small and the scenes are large, indicating that the intersection over union value between the targets and anchors is probably small. In addition, SAR images are severely polluted by speckles under normal conditions. The edges
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Stochastic radiation field optimization for microwave staring correlated imaging via spatial correlation minimization J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Zheng Jiang; Jianlin Zhang; Bo Yuan; Yuanyue Guo; Dongjin Wang
Microwave staring correlated imaging (MSCI) is a staring high-resolution microwave imaging technique, employing the temporal–spatial stochastic radiation field (TSSRF). The imaging capability of MSCI depends on the spatial correlation of the TSSRF, which is equivalent to the incoherence of the sensing matrix in discrete form. A waveform design method for MSCI using multifrequency signals to reduce
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Characterizing ecosystem functional type patterns based on subtractive fuzzy cluster means using Sentinel-2 time-series data J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Rong Liu; Fang Huang; Yue Ren; Ping Wang; Jing Zhang
The characteristics of ecosystem functions are of great significance for biodiversity conservation and ecosystem services. Ecosystem functional types (EFTs) are land surface areas similar in carbon dynamics that are not defined by the structure and composition of vegetation and represent the spatial heterogeneity of ecosystem functions. However, identification of EFTs based on low-resolution remote
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Differential synthetic aperture radar interferometric phase map despeckling in discrete Riesz wavelets domain J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Jamila Fathi; Khalid Ghzala; Elhoucaine Elkharrouba; Yassine Tounsi; Ahmed Siari; Hamid Bioud; Abdelkrim Nassim
Phase extraction in differential synthetic aperture radar (SAR) interferometry (DInSAR) is an important tool used for detecting subcentimeter-level change in ground deformation. The evaluated phase map processing is conducted via two important and successive steps: phase denoising and phase unwrapping. We attack the first step and propose the performance of discrete Riesz wavelets transform to reduce
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GoSPo: a goniospectropolarimeter to assess reflectance, transmittance, and surface polarization as related to leaf optical properties J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Reisha D. Peters; Simone R. Hagey; Scott D. Noble
Visible-near infrared (VIS-NIR) spectral data are widely used for remotely estimating a number of crop health metrics. In general, these indices and models do not explicitly account for leaf surface characteristics, which themselves can be indicators of plant status or environmental responses. To explicitly include leaf surface characteristics, data are required linking optical properties to surface
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Airborne radiometric validation of the geostationary lightning mapper using the Fly’s Eye GLM Simulator J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Mason G. Quick; Hugh J. Christian; Katrina S. Virts; Richard J. Blakeslee
The Fly’s Eye GLM Simulator (FEGS) is a multi-spectral array of radiometers designed to provide a validation dataset for the geostationary lightning mapper (GLM). The main component of FEGS is a 5 × 5 grid of radiometers, each with a square 18 deg field of view, that are sensitive to a 10-nm wide spectral band centered on 780 nm to observe a neutral atomic oxygen emission triplet at 777.4 nm. FEGS
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Approach to enhance trace gas determinations through multi-satellite data fusion J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Elisabeth Weisz; W. Paul Menzel
The imaging instruments on the polar-orbiting S-NPP and NOAA-20 satellite platforms [e.g., Visible Infrared Imaging Radiometer Suite (VIIRS)] and on geostationary GOES and Himawari platforms [e.g., Advanced Baseline Imager (ABI) and Advanced Himawari Imager (AHI)] have high horizontal spatial resolution but coarse vertical information about tropospheric gases and temperatures. An approach for fusing
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Validation of GOES-16 ABI VNIR channel radiometric performance with NPP and NOAA-20 VIIRS over the Sonoran Desert J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Xin Jing; Tung-Chang Liu; Xi Shao; Sirish Uprety; Bin Zhang; A. Surjalal Sharma
The advanced baseline imager (ABI) onboard Geostationary Operational Environmental Satellites-16 (GOES-16) provides high-quality visible and near-infrared (VNIR) imagery data. Radiometric performance of the GOES-16 ABI multiple VNIR bands (B1, B2, B3, B5, and B6) is evaluated over the Sonoran Desert by comparing measurements with Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 visible
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Remote-sensing image super-resolution using classifier-based generative adversarial networks J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Haosong Yue; Jiaxiang Cheng; Zhong Liu; Weihai Chen
The rapid development of the aerospace industry has significantly increased the demand for remote-sensing images with high resolution and quality. Generating images with expected resolution from the samples obtained by common acquisition devices is a challenging task as the trade-off between cost and efficiency must be considered. We propose a super-resolution (SR) algorithm especially for remote-sensing
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Comparison of pixel- and object-based image analysis for tea plantation mapping using hyperspectral Gaofen-5 and synthetic aperture radar data J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Yujia Chen; Shufang Tian
Accurately mapping tea plantation distribution is crucial to environmental protection and sustainable development. Hyperspectral and synthetic aperture radar (SAR) data have recently been widely used in land cover classification, but their ability to extract tea plantation regions still needs to be confirmed. Compared with traditional pixel-based image analysis (PBIA), object-based image analysis (OBIA)
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Generalized likelihood ratio test for optical subpixel objects’ detection with hypothesis-dependent background covariance matrix J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Victor Golikov; Oleg Samovarov; Evgeniy Zhilyakov; Jose L. Rullan-Lara; Hussain Alazki
Much interest has arisen in the problem of detecting weak optical subpixel objects in a sequence of images immersed in a heavy homogeneous Gaussian clutter background. In optical systems, the presence of the objects changes the background plus the channel noise covariance matrix. Hence, this matrix may be different under null and alternative hypotheses. Because the maximum likelihood estimate of the
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Landscape fragmentation in coffee agroecological subzones in central Kenya: a multiscale remote sensing approach J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Gladys Mosomtai; John Odindi; Elfatih M. Abdel-Rahman; Régis Babin; Pinard Fabrice; Onisimo Mutanga; Henri E. Z. Tonnang; Guillaume David; Tobias Landmann
Smallholder agroecological subzones (AEsZs) produce an array of crops occupying large areas throughout Africa but remain largely unmapped. We explored multisource satellite datasets to produce a seamless land-use and land-cover (LULC) and fragmentation dataset for upper midland (UM1 to UM4) AEsZs in central Kenya. Specifically, the utility of PlanetScope, Sentinel 2, and Landsat 8 images for mapping
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Multi-sensor study of precipitable water vapor and atmospheric profiling from microwave radiometer, GNSS/MET, radiosonde, and ECMWF reanalysis in Beijing J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Heng Hu; Rongkang Yang; Wen-Chau Lee; Yunchang Cao; Jiajia Mao; Lina Gao
We compare the precipitable water vapor (PWV) determined using a domestic ground-based microwave radiometer (MWR PWV) with PWV measurements from radiosondes (RS PWV), the Global Navigation Satellite System (GNSS PWV), and reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) (EC PWV). The MWR PWV is affected by precipitation, and thus it differs greatly from the other three
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Visible and near-infrared spectroscopy for detection of powdery mildew in Cucurbita pepo L. leaves J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Claudia Angelica Rivera-Romero; Elvia Ruth Palacios-Hernández; Monica Trejo-Durán; Maria del Carmen Rodríguez-Liñán; Roberto Olivera-Reyna; Jorge Alberto Morales-Saldaña
Cucurbits plants are very susceptible to fungal diseases as powdery mildew (PM) infection. Currently, the PM early detection in the field is the cutting-edge research. The objectives of our study were to assess visible and near-infrared spectroscopy of normal and infected leaves for early detection of PM in Cucurbita pepo L. plants. Samples for spectral analysis were taken three days a week (Monday
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Four-directional spatial regularization for sparse hyperspectral unmixing J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Touseef Ahmad; Rosly Boy Lyngdoh; Anand S. Sahadevan; Soumyendu Raha; Praveen Kumar Gupta; Arundhati Misra
A four-directional total variation technique is proposed to encapsulate the spatial contextual information for sparse hyperspectral image (HSI) unmixing. Traditional sparse total variation techniques explore gradient information along with the horizontal and vertical directions. As a result, spatial disparity due to high noise levels within the neighboring pixels are not considered while unmixing.
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Image registration and selection for unmanned aerial vehicle image stitching J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Junxing Yang; Lulu Liu; Lu Lu; Fei Deng
In this study, a general transformation-based framework for unmanned aerial vehicle image stitching is proposed that can resist global distortion while improving the local registration accuracy. In the first step, with tie points as constraints, the global transformation function of each image is obtained in an optimization manner and no reference image is needed. In the second step, to reduce data
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Remotely sensed characterization of Acacia longifolia invasive plants in the Cape Floristic region of the Western Cape, South Africa J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-11-01 Cletah Shoko; Onisimo Mutanga; Timothy Dube
Acacia longifolia, like any other invasive species, poses a threat to the natural ecosystems in South Africa and beyond. In South Africa, the species is reported to cause significant loss to vegetation biodiversity and is associated with high water use. It remains a challenge to understand the extent of their invasion in complex landscapes, where the use of ground-based surveys is difficult. Considering
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Improved U-Nets with inception blocks for building detection J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-11-01 Ibrahim Delibasoglu; Mufit Cetin
With the rapid increase of the world’s population, urban growth management and monitoring have become an important component in environmental, social, and economic terms. In general, automatic detection of buildings in urban areas from high-resolution satellite imagery has become an important issue. In recent years, the U-Net architecture has become one of the most popular convolutional neural networks
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Joint detection and tracking of extended stealth targets from image observations based on subrandom matrices J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-11-01 Mohamed Barbary; Mohamed H. Abd El-Azeem
The challenge of joint detection and tracking of multiple extended targets (ETs) arises in many radar applications, especially for extended stealth targets (ESTs) with small signal-to-noise ratio (SNR). Recently, the multi-Bernoulli (MB) filter-based random matrix model (RMM) has been proposed for tracking ellipsoidal ETs. The MB-RMM filter depends on the measurements (position and range rate), which
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Volume coherence function optimization method for extracting vegetation and terrain parameters from polarimetric synthetic aperture radar interferometry images J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-11-01 Tan N. Nguyen; Pham M. Nghia; Thieu H. Cuong; Van N. Le
An advanced algorithm is introduced to enhance the efficiency in measuring vegetation parameters using L-band polarimetric synthetic aperture radar interferometry data. In this method, the combination of the eigenvalue decomposition technique and the coherence matrix optimization method is to achieve higher accuracy for ground phase estimation. Then an exhaustive search method based on the optimal
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Deep convolutional neural network for P-band spaceborne synthetic aperture radar imaging through the ionosphere J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-11-01 Hongyin Shi; Jing Zhang; Er-Fang Gao; Ting Yang; Jianwen Guo
The dispersion characteristics of the background ionosphere and the random fluctuations of the ionospheric irregularities are an important source of phase error that seriously damages the quality of radar images. To mitigate the ionospheric distortions of P-band spaceborne synthetic aperture radar (SAR) images, a super-resolution deep learning method is proposed in this paper. Different from the traditional
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Multi-channel space-time decorrelation analysis method based on sea clutter J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-11-01 Yu Li; Yuan Zhou; Weiwei Wang; Caipin Li; Chongdi Duan; Xuyan Wang
In sea clutter background, spaceborne or airborne surveillance radar with a high-speed moving platform is confronted with the problem of clutter spreading. This poses a considerable challenge for maritime radar systems when detecting slowly moving targets of low signal-to-noise ratio. However, the space-time decorrelation characteristics caused by the internal motions of sea clutter under different
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Alternating direction method-based endmember extraction for a distributed fraction cover mapping of mineralogy at Jahazpur, India J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-11-01 Sukanta Roy; Satadru Bhattacharya; Subbaramajois Narasipur Omkar
The quantification of mineral resources refers to the fractional contribution of endmembers at the pixel level, namely, fraction cover mapping of mineralogy. Over a large area, the mineral deposit occurs generally in a limited number either on a host rock or any geologic structure. In remote sensing, the purity of mineral’s spectra is usually perturbed either because of the weathering effect or the
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Three-dimensional micromotion trajectory reconstruction of rotating targets by interferometric radar J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-11-01 Wenwu Kang; Yunhua Zhang; Xiao Dong; Jiefang Yang
Imaging, feature extraction, and recognition of targets with micromotion by retrieving their three-dimensional micromotion trajectories (3-D MMTs) have attracted a lot of interest in recent years. We propose a method for retrieving the 3-D MMT of a rotating target based on the interferometric phases obtained using a wideband interferometric radar system with three antennas positioned in L-shape. First
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C-band dual-polarization Doppler weather radar at Thumba (8.537°N, 76.865°E): initial results and validation J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-11-01 Karanam Kishore Kumar; Kandula V. Subrahmanyam; Chitla Pradeep Kumar; Jeyaram Shanmugasundari; Neelakantan Koushik; Raju P. Ajith; Lekshmykutty Girija Devi
Recently, a C-band dual-polarization Doppler weather radar (C-DWR) was installed at Thumba (8.537°N, 76.865°E), a coastal station in the southern peninsula of India known as “Gateway of Indian Summer Monsoon.” The C-DWR operates in the frequency range of 5.6 to 5.65 GHz with a peak transmitting power of 250 kW at 0.004 duty ratio and employs the state-of-the-art technology for both transmitting and
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Feature evaluation for land use and land cover classification based on statistical, textural, and shape features over Landsat and Sentinel imagery J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-11-01 Abel Coronado; Daniela Moctezuma
The use of remote sensing data has become very useful to generate statistical information about society and its environment. In this sense, land use and land cover classification (LULC) are tasks related to determining the cover on the Earth’s surface. In the decision-making process, this kind of information is relevant to handle in the best way how the information about events such as earthquakes
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3D convolutional siamese network for few-shot hyperspectral classification J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-11-01 Zeyu Cao; Xiaorun Li; Jianfeng Jiang; Liaoying Zhao
Hyperspectral classification is a widely discussed problem in the remote sensing field. Many researchers have reported good results of hyperspectral classification. However, when applied to the real world, the strong demand for labeled data for hyperspectral classification will be a big obstacle. To address this problem, researchers have explored few-shot learning and semisupervised methods in a variety
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