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A method of water change monitoring in remote image time series based on long short time memory Remote Sens. Lett. (IF 2.298) Pub Date : 2021-01-07 Qiyuan Yang; Chuanjian Wang; Tiaojun Zeng
ABSTRACT This paper proposes convolutional neural network jointed with long short-time memory (CNN_LSTM) and Seq2Seq based on convolutional operation (Convolutional Seq2Seq), which the fully connected operation of Seq2Seq is replaced by convolution, and the attention mechanism of Seq2Seq is improved to monitor changes in water bodies. Convolutional Seq2Seq and CNN_LSTM can extract the temporal and
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A non-local capsule neural network for hyperspectral remote sensing image classification Remote Sens. Lett. (IF 2.298) Pub Date : 2021-01-07 Runmin Lei; Chunju Zhang; Shihong Du; Chen Wang; Xueying Zhang; Hui Zheng; Jianwei Huang; Min Yu
ABSTRACT In this study, we introduce a non-local block of the attention mechanism into capsule neural network (CapsNet) to form a non-local capsule network (NLCapsNet) for hyperspectral remote sensing image (HSI) classification. The presented NLCapsNet uses global information from input images and has a powerful representation of the capacity and spatial relationships among HSI features. It can effectively
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A time-domain approach to the total ozone time series and a test of its predictability within a univariate framework Remote Sens. Lett. (IF 2.298) Pub Date : 2021-01-05 Sombit Chakraborty; Surajit Chattopadhyay
ABSTRACT The present study reports a univariate predictive model for Total Ozone (TO) concentration derived from Ozone Monitoring Instrument (OMI)/Aura observations. Using the Markovian approach through proper discretization method it has been observed that the second-order Markov Chain can represent the time series of TO Concentration. Considering daily data spanning from 2015 to 2019, consisting
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Low complexity single dataset STAP for nonstationary clutter suppression in HF mixed-mode surface wave radar Remote Sens. Lett. (IF 2.298) Pub Date : 2020-12-03 Jiazhi Zhang; Xin Zhang; Weibo Deng; Liang Guo; Qiang Yang
ABSTRACT The nonstationary clutter is one of the biggest challenge to ocean remote sensing radar system such as high frequency (HF) mixed-mode surface wave radar. The performance of space-time adaptive processing (STAP) degrades badly with limited homogeneous secondary training data support. Single dataset algorithms overcome the problem by working on primary data solely. But the heavy computational
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A novel phase bias estimation method for multichannel HRWS SAR system in azimuth based on RDS analysis Remote Sens. Lett. (IF 2.298) Pub Date : 2020-12-03 Jianhui Zhao; Kuan Wang; Ling Wang; Zhengwei Guo; Ning Li
ABSTRACT Multichannel synthetic aperture radar (SAR) systems in azimuth can realize high-resolution and wide-swath (HRWS) imaging, which has attracted intensive research interests. The azimuth ambiguities caused by non-uniform sampling can be suppressed by digital beamforming technique. However, the presence of inevitable channel errors in the multichannel SAR system deteriorates the performance of
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MODIS aerosol optical depth retrieval based on random forest approach Remote Sens. Lett. (IF 2.298) Pub Date : 2020-12-06 Tianchen Liang; Lin Sun; Haoxin Li
ABSTRACT Despite significant improvement in Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval, high-resolution–high-accuracy AOD retrieval remains a challenging task. This study utilises machine learning for AOD retrieval of MODIS data. The global long-time-series data of AERONET sites and corresponding MODIS data in time and space were used as sample training
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Spatio-temporal reductions of the COVID-19 lockdown-induced noise anomalies in GNSS height time series over mainland China Remote Sens. Lett. (IF 2.298) Pub Date : 2020-12-03 Chaolong Yao; Peitong Cong; Jiakuan Wan; Tingting Li; Lilong Liu; Changwei Wang; Rui Zhang; Yifeng Fu; Tao Lin; Chuang Xu; Xu Lin
ABSTRACT The lockdowns imposed worldwide to curb the coronavirus diseases (COVID-19) spread has positive effects on the environment. However, it is unclear what fraction is caused by weather and what is related to lockdown. Here we used Global Navigation Satellite System (GNSS) height anomaly time series to quantify the spatio-temporal characteristics of lockdown-induced noise anomalies at 231 selected
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A modified TSM for better prediction of hh polarized microwave backscattering coefficient from sea surface Remote Sens. Lett. (IF 2.298) Pub Date : 2020-12-02 Honglei Zheng; Jie Zhang; Yanmin Zhang; Ali Khenchaf; Yunhua Wang
ABSTRACT ABSTRACT: The two-scale Model (TSM) is broadly employed for studying EM scattering from rough sea surface due to its advantage of simple and practical. However, it suffers from a drawback in the prediction of microwave scattering from sea surface. That is the TSM cannot provide accurate predictions for hh polarized scattering coefficients. To overcome this problem, a modified TSM (MTSM) is
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Evaluation of different approaches to the fusion of Sentinel -1 SAR data and Resourcesat 2 LISS III optical data for use in crop classification Remote Sens. Lett. (IF 2.298) Pub Date : 2020-12-02 Neetu; Shibendu Shankar Ray
ABSTRACT This study evaluates various combinations of data fusion techniques at Pixel, Feature, and Decision level for crop classification using Sentinel-1 Synthetic Aperture Radar (SAR) data and Resourcesat-2 LISS (Linear Imaging Self Scanning) III, optical data for Yadgir District of Karnataka, India. For Pixel level data fusion, techniques such as brovey transformation (BT), principal component
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An experimental study on the influence of copper and lead concentration on the spectral reflectance of maize leaves Remote Sens. Lett. (IF 2.298) Pub Date : 2020-12-01 Chao Zhang; Keming Yang; Wei Gao
ABSTRACT If crops were polluted by heavy metals, the spectra will change. Therefore, the variation information of spectral changes has become an important basis for heavy metal pollution monitoring. Based on spectral frequency domain, we studied the spectra of maize leaves. Combined with time-frequency analysis method, we proposed DDCR-Db (Second-order Differential Continuum Removal- Daubechies) method
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The vertical structure of latent heating and its association with cloud types during the Indian summer monsoon Remote Sens. Lett. (IF 2.298) Pub Date : 2020-11-19 Kandula Subrahmanyam; K. Kishore Kumar
ABSTRACT In the present communication, using cloud-type distribution derived from CloudSat observations during the years 2006–2010 and vertical structure of LH derived from Tropical Rain Measuring Mission (TRMM) observations during the same period over the Indian summer monsoon (ISM) region, an attempt is made to relate the both. First, the regions where particular type of clouds forms predominantly
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Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep convolutional neural network Remote Sens. Lett. (IF 2.298) Pub Date : 2020-11-19 Zhixiang Yin; Feng Ling; Giles M. Foody; Xinyan Li; Yun Du
ABSTRACT Google Earth Engine (GEE) provides a convenient platform for applications based on optical satellite imagery of large areas. With such data sets, the detection of cloud is often a necessary prerequisite step. Recently, deep learning-based cloud detection methods have shown their potential for cloud detection but they can only be applied locally, leading to inefficient data downloading time
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A New Index for Assessing Tree Vigour Decline Based on Sentinel-2 Multitemporal Data. Application to Tree Failure Risk Management Remote Sens. Lett. (IF 2.298) Pub Date : 2020-11-18 De Petris S.; Sarvia F.; Borgogno-Mondino E.
ABSTRACT Detection and monitoring of vigour decline are certainly useful for forest managers to support future interventions. In this work a new index, from Sentinel-2 retrieved NDVI (Normalized Difference Vegetation Index) time series, is proposed, hereafter called ‘normalized Vegetation Vigour Index (nVVI)’, which is specifically designed to quantify and map tree vigour decline. Results proved that
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Detection of internal waves in the Persian Gulf Remote Sens. Lett. (IF 2.298) Pub Date : 2020-11-17 Sajad Andi; Akbar Rashidi Ebrahim Hesari; Hossein Farjami
ABSTRACT Identification and characteristics of Internal Waves (IWs) are widely used in a variety of industries, including shipbuilding, maritime, as well as oil and marine engineering. In this study, IWs have been detected using field measurement and satellite remote sensing in the Persian Gulf during the period 2000–2015. In order to identify the potential areas for IWs after vertical changes in the
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Evaluation of simulated soil moisture from China Land Data Assimilation System (CLDAS) land surface models Remote Sens. Lett. (IF 2.298) Pub Date : 2020-10-20 Yuanyuan Wang; Guicai Li
ABSTRACT This study evaluated the land surface model-simulated soil moisture (SM) product from the China Land Data Assimilation System (CLDAS). This was achieved using three densely instrumented in situ observation networks in China that have different environmental conditions, i.e., the Hebi, Naqu and Heihe sites. The European Space Agency Climate Change Initiative multi-satellite-retrieved SM product
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Fully convolutional neural nets in-the-wild Remote Sens. Lett. (IF 2.298) Pub Date : 2020-10-20 Daniel M. Simms
ABSTRACT The ground breaking performance of fully convolutional neural nets (FCNs) for semantic segmentation tasks has yet to be achieved for landcover classification, partly because a lack of suitable training data. Here the FCN8 model is trained and evaluated in real-world conditions, so called in-the-wild, for the classification of opium poppy and cereal crops at very high resolution (1 m). Densely
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Development and evaluation of regional SST regression algorithms for FY-3C/VIRR data in the western north pacific Remote Sens. Lett. (IF 2.298) Pub Date : 2020-10-20 Quanjun He; Yuewei Zhang; Jiechun Wang
ABSTRACT Satellite remote sensing has been one of the most main methods to acquire the sea surface temperature (SST). In this study, the new regional algorithms to estimate SST in the western north Pacific are developed using the data from the visible and infrared radiometer (VIRR) aboard the Chinese FengYun-3 C (FY-3 C) meteorological satellite. The new regional algorithms are based on the nonlinear
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Selection of multi-view SAR images via nonlinear correlation information entropy with application to target classification Remote Sens. Lett. (IF 2.298) Pub Date : 2020-10-20 Chenyu Li; Haoping Qi
ABSTRACT A synthetic aperture radar (SAR) target classification method is proposed by properly selecting multiple views via the nonlinear correlation information entropy (NCIE). The optimal subset of multi-view SAR images are selected, which are assumed to share stable inner correlations. The joint sparse representation is adopted as the basic classification scheme for the selected views to exploit
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The impact of cloud masking on the climatology of sea surface temperature gradients Remote Sens. Lett. (IF 2.298) Pub Date : 2020-10-26 M. Bouali; P. S. Polito; O. T. Sato; P. S. Bernardo; J. Vazquez-Cuervo
ABSTRACT Cloud masking is a critical step in the estimation of Sea Surface Temperature (SST) from satellite observations. It can affect the validation statistics of SST on synoptic scales but also on long-term climatologies. One of the main challenges in cloud masking is the discrimination between clouds and ocean sharp fronts as both of these are associated with high spatial variability. In this study
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Classifying open water features using optical satellite imagery and an object-oriented convolutional neural network Remote Sens. Lett. (IF 2.298) Pub Date : 2020-10-20 Michael A. Merchant
ABSTRACT In this study, Sentinel-2 optical satellite imagery was acquired over the Peace Athabasca Delta and assessed for its open water classification capabilities using an object-oriented deep learning algorithm . The workflow involved segmenting the satellite data into meaningful image objects, building a Convolutional Neural Network (CNN), training the CNN, and lastly applying the CNN, resulting
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Target discrimination method for SAR images via convolutional neural network with semi-supervised learning and minimum feature divergence constraint Remote Sens. Lett. (IF 2.298) Pub Date : 2020-10-25 Ning Wang; Yinghua Wang; Hongwei Liu; Qunsheng Zuo
ABSTRACT Target discrimination is an important part of the synthetic aperture radar automatic target recognition (SAR ATR). Nowadays, convolutional neural network (CNN) has been used in SAR ATR successfully. However, training CNN requires large amounts of labelled data and collection of the labelled SAR data is expensive and time demanding. It may yield overfitting when directly applying CNN to the
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Forest insect defoliation and mortality classification using annual Landsat time series composites: a case study in northwestern Ontario, Canada Remote Sens. Lett. (IF 2.298) Pub Date : 2020-10-20 Steven Franklin; Sheryl Robitaille
ABSTRACT Landsat satellite time series annual Best-Available-Pixel (BAP) composites for the period 1984–2017 of the Kenora Forest Management Unit in northwestern Ontario, Canada were sampled and stratified by forest stand conditions and aerial sketch map (ASM) compilations of mortality and defoliation. Pre- and post-disturbance multispectral image and textural data were classified using a logistic
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Estimating diameter at breast height using personal laser scanning data based on stem surface nodes in polar coordinates Remote Sens. Lett. (IF 2.298) Pub Date : 2020-10-15 Jialong Duanmu; Yanqiu Xing
ABSTRACT Personal laser scanning (PLS) has shown great potential in diameter of breast height (DBH) estimation. Compared with the current laser scanning technique, PLS is hardly restricted by occlusion and trafficability, and produces omnidirectional point cloud data. However, the DBH estimation accuracy using PLS data was not higher in the previous research. One of the leading sources of bias is overlapping
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Retrieving fPAR of maize canopy using artificial neural networks with airborne LiDAR and hyperspectral data Remote Sens. Lett. (IF 2.298) Pub Date : 2020-09-24 Juncheng Shi; Cheng Wang; Xiaohuan Xi; Xuebo Yang; Jinliang Wang; Xue Ding
Accurate estimation of the fraction of absorbed photosynthetically active radiation (fPAR) is important for maize growth and yield estimations. Light detection and ranging (LiDAR)-derived canopy vertical structural and hyperspectral image-derived vegetation spectral information are complementary for vegetation fPAR estimation. This study explores the potential of artificial neural networks (ANNs) with
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Road extraction from aerial image data via multiple features integrated with convolution long short time memory unit network Remote Sens. Lett. (IF 2.298) Pub Date : 2020-09-24 Fei Huang; Zhengcai Liu; Ting Xie
Semantic segmentation models based on deep learning have shown remarkable performance in road extraction from high-resolution aerial images. However, it is still a difficult task to segment multiscale roads with high completeness and accuracy from complex backgrounds. To deal with this problem, this letter proposes an end to end network named Multiple features integrated with convolutional long-short
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Selecting image pairs for structure-from-motion by introducing the image spatial position Remote Sens. Lett. (IF 2.298) Pub Date : 2020-09-24 Chaofeng Ren; Shuai Yang; Hongli Ma; Xiaodong Peng; Junkai Gu
Image matching is a quite time-consuming task for Structure-from-Motion (SfM). In this paper, a Bag-of-Words (BoW) model that reduces the feature dimensions and introduces image spatial locations is proposed to improve the efficiency and reliability of SfM. The whole workflow includes three steps. Firstly, principal component analysis (PCA) is used to reduce the high-dimensional features to low-dimensional
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Estimating the vertical distribution of chlorophyll in winter wheat based on multi-angle hyperspectral data Remote Sens. Lett. (IF 2.298) Pub Date : 2020-09-24 Lin Wang; Qinhong Liao; Xiaobin Xu; Zhenhai Li; Hongchun Zhu
Chlorophyll plays an important role in crop photosynthesis, which is closely related to nitrogen (N). N deficiency first occurs in the lower leaves, but the spectral detection of the lower layer is insufficient due to leaf shading. The aim of this paper was to investigate the feasibility of estimating the chlorophyll content of leaves (LCC) and the vertical distribution of LCC in wheat using multi-angle
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Ridgeline extraction only from a single full-polarimetric SAR image Remote Sens. Lett. (IF 2.298) Pub Date : 2020-09-24 Haoran Zhang; Wei Zhai; Xiulai Xiao; Shaohua Li
The mountain hazard triggered by earthquake usually starts near the ridgeline. Knowing the distribution of the ridgeline is conducive to making sound judgements on the possible hazards after an earthquake. However, in the complex scenes of PolSAR images containing buildings, the ridgeline recognition process will produce incorrect judgements due to the existence of buildings. Aiming at the ridgeline
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An early exploration of the use of the Microsoft Azure Kinect for estimation of urban tree Diameter at Breast Height Remote Sens. Lett. (IF 2.298) Pub Date : 2020-09-22 James McGlade; Luke Wallace; Bryan Hally; Andrew White; Karin Reinke; Simon Jones
Forest and urban tree inventory measurements are increasingly adopting Remote Sensing (RS) techniques due to the accurate and rapid estimates available compared to conventional methods. The focus of this study is to assess the accuracy and potential application of the Microsoft Azure Kinect – a lightweight depth sensor – for outdoor measurement of tree stem Diameter at Breast Height (DBH). Individual
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Open surface water index: a novel approach for surface water mapping and extraction using multispectral and multisensory data Remote Sens. Lett. (IF 2.298) Pub Date : 2020-09-22 Vikash Kumar Mishra; Triloki Pant
In the present study, a novel water index has been proposed for mapping of open surface water in multispectral images and named as open surface water index (OSWI). The proposed index has been applied on Landsat-8 data to delineate the water area and the results are compared with those of existing water indices. The index is also applied on Sentinel-2A data and corresponding water pixels are extracted
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A mixture generative adversarial network with category multi-classifier for hyperspectral image classification Remote Sens. Lett. (IF 2.298) Pub Date : 2020-09-22 Hengchao Li; Weiye Wang; Shaohui Ye; Yangjun Deng; Fan Zhang; Qian Du
Hyperspectral image (HSI) classification is one of the core techniques in HSI processing. In order to solve the problem of scarcity of labelled samples, a novel HSI classification framework based on mixture generative adversarial networks (MGAN) is proposed in this letter. Firstly, to overcome the drawback that MGAN cannot be directly applied for classification, a category multi-classifier is introduced
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Heartbeat monitoring with an mm-wave radar based on deep learning: a novel approach for training and classifying heterogeneous signals Remote Sens. Lett. (IF 2.298) Pub Date : 2020-09-22 Haoyu Zhang
Millimetre wave radar is an emerging technology that can monitor vital signs without contact. This unique feature is very suitable for some particular situations, such as burn patient monitoring. Currently, electrocardiogram (ECG) is still the most common approach for monitoring heart disease. Deep learning algorithms have already been applied to classifying ECG recordings and have achieved good diagnostic
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Estimation of volumetric surface soil moisture content using microwave-calibrated soil evaporative efficiency information Remote Sens. Lett. (IF 2.298) Pub Date : 2020-07-29 Hongling Xiu; Wenbin Zhu; Fengyun Yang; Jiaxing Wei; Feng Wang
Soil moisture (SM) is a critical variable in energy and water partitioning at the interface between the land surface and atmosphere. In this study, we provided a robust method to retrieve soil moisture using optimal remotely sensed soil evaporative efficiency (SEE) information. Specifically, SEE was deduced from the triangle space constituted by remotely sensed land surface temperature (LST) and fractional
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Quantifying chlorophyll-a and b content in tea leaves using hyperspectral reflectance and deep learning Remote Sens. Lett. (IF 2.298) Pub Date : 2020-07-29 Rei Sonobe; Yuhei Hirono; Ayako Oi
To improve the quality of green tea, low light stress has been used to increase the chlorophyll-a (chl-a) content of tea leaves, although shading treatments sometimes lead to early mortality of tea trees. Therefore, in situ measurement of chl-a and chlorophyll-b (chl-b), which are markers for evaluating light stress and response to changing environmental conditions, can be used to improve tea tree
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A test of a sun glint correction method for the near-3.9 μm channels of the FengYun-3D Hyperspectral InfraRed Atmospheric Sounder (HIRAS) Remote Sens. Lett. (IF 2.298) Pub Date : 2020-07-29 Liwen Wang; Zeyi Niu; Banglin Zhang; Fei Tang; Jiahao Liang
The Hyperspectral InfraRed Atmospheric Sounder (HIRAS), the first Chinese sun-synchronous hyperspectral infrared sounder onboard FengYun 3D (FY-3D), was launched on 15 November 2017. It will play a significant role in improving forecast accuracy of numerical weather prediction (NWP) systems. However, sun glint effects on the FY-3D HIRAS 3.9 μm channels cannot be currently estimated by radiative transfer
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Matting model guided variational pansharpening with fractional order gradient transferring Remote Sens. Lett. (IF 2.298) Pub Date : 2020-07-29 Pengfei Liu; Songze Tang; Lili Huang
In this letter, we proposed a matting model guided variational pansharpening method with fractional order gradient transferring constraint to fuse a low-resolution (LR) multispectral (MS) and a high-resolution (HR) panchromatic (Pan) image to an HR-MS image. Specifically for the proposed model, we not only used the commonly local spectral fidelity constraint for spectral information preserving, but
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Accuracy validation and bias assessment for various multi-sensor open-source DEMs in part of the Karakoram region Remote Sens. Lett. (IF 2.298) Pub Date : 2020-07-24 Anant Kumar; H.S. Negi; Kamal Kumar; Chander Shekhar
The present study evaluate horizontal and vertical accuracy of seven open-source digital elevation models (DEMs) having moderate-to-high resolutions viz. 30 m Shuttle Radar Topography Mission (SRTM1), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), Advanced Land Observing Satellite World 3D (AW3D30), and Cartosat DEM (CartoDEM), 90 m TerraSAR-X add-on for Digital
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Deriving bathymetry and water properties from hyperspectral imagery by spectral matching using a full radiative transfer model Remote Sens. Lett. (IF 2.298) Pub Date : 2020-07-24 David B. Gillis; Jeffrey H. Bowles; Marcos J. Montes; W. David Miller
Many existing techniques for estimating the optical properties of a body of water directly from a hyperspectral remote sensing spectrum are based on the idea of ‘spectral matching’ – that is, input parameters in a forward model are systematically varied until the measured and modelled spectra are sufficiently similar. This is usually done by using numerical optimization methods with a simplified forward
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Estimation of ground-level dry PM2.5 concentrations at 3 km resolution over Beijing using Geostationary Ocean Colour Imager Remote Sens. Lett. (IF 2.298) Pub Date : 2020-07-24 Jingwei Wang; Zhengqiang Li
In this study, aerosol optical depth (AOD) and fine-mode fraction (FMF) with a 3 km resolution are retrieved from Geostationary Ocean Colour Imager (GOCI) data using a multi-temporal method. The retrieved results are input into a physical model for estimating the fine particulate matter (PM2.5) based on instantaneous satellite-based measurements. The other two input parameters of the model are planetary
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Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape Remote Sens. Lett. (IF 2.298) Pub Date : 2020-07-09 Mahlatse Kganyago; Paidamwoyo Mhangara; Thomas Alexandridis; Giovanni Laneve; Georgios Ovakoglou; Nosiseko Mashiyi
ABSTRACT This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and inter-comparison experiments were performed on two processing levels, i.e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency
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Comparison of water cloud models with different layers for rice yield estimation from a single TerraSAR image Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-29 Xuexiao Wu; Long Liu; Xianyu Guo; Zhiqu Liu; Kun Li; Yun Shao
This paper discusses the estimation of rice yield from Water Cloud Models (WCM) with different layers by a single X-band (8–12 GHz) Synthetic Aperture Radar (SAR) image, from a satellite named TerraSAR. A simulated annealing (SA) algorithm, with a ‘leave-one-out’ cross-validation method, was applied. The results were related to actual rice parameters, with a coefficient of determination (R 2) of 0
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A feature sequence-based 3D convolutional method for wetland classification from multispectral images Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-27 Hong Pan
As an important part of the ecosystem, wetlands provide varies of ecological functions while they have also been increasingly threatened and degraded. Therefore, it is necessary to protect wetlands with effective monitoring measures such as classification. Considering the quick development of multispectral sensors, multispectral images are available to classify wetlands. The current method, however
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Tree extraction from multi-scale UAV images using Mask R-CNN with FPN Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-27 Nuri Erkin Ocer; Gordana Kaplan; Firat Erdem; Dilek Kucuk Matci; Ugur Avdan
Tree detection and counting have been performed using conventional methods or high costly remote sensing data. In the past few years, deep learning techniques have gained significant progress in the remote sensing area. Namely, convolutional neural networks (CNNs) have been recognized as one of the most successful and widely used deep learning approaches and they have been used for object detection
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Modification of generalized BDSD framework using joint products of spectral bands Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-27 Lifeng Wang; Ziwang Xiao; Saisai Yu; Huijiang Qu; Rifaat Abdalla
In this paper, a novel modification applies to the Band-Dependent Spatial Detail (BDSD) framework to improve the fusion performance interestingly. Joint multiplication terms have been added to the conventional BDSD framework to make it nonlinear BDSD. It has been shown that joint product terms would lead to a better estimation of detail maps for each spectral band. To support the idea presented here
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Improved quantum evolutionary particle swarm optimization for band selection of hyperspectral image Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-27 Lei Yu; Yifei Han; Linlin Mu
The large number of bands, the huge amount of information and the high band correlation in hyperspectral images bring great difficulties to the band selection of hyperspectral images. In the basic Particle Swarm Optimization (PSO), the learning factor and inertia factor are fixed, which limits the exploratory ability of the algorithm and cannot balance the global and local relations well. Therefore
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Snow depth and snow water equivalent retrieval using X-band PolInSAR data Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-25 Akshay Patil; Shradha Mohanty; Gulab Singh
Monitoring the dynamics of snow and glaciers in the Indian Himalaya has always attracted the attention of the remote sensing community. The snow water equivalent (SWE) represents the amount of water contained in the snowpack, and it is a product of snow depth (SD) and snow density ( ρ s ). The estimation of SD at high spatial and temporal resolution is still a challenge, especially in rugged mountainous
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Deep CNN-based hyperspectral image classification using discriminative multiple spatial-spectral feature fusion Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-25 Hao Guo; Jianjun Liu; Zhiyong Xiao; Liang Xiao
Convolutional Neural Networks (CNNs) are widely used in various fields, and have shown good performance in hyperspectral image (HSI) classification. Recently, utilizing deep networks to learn spatial-spectral features has become of great interest. However, excessively increasing the depth of network may result in overfitting. Moreover, in HSI classification, the existing network models ignore the strong
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Improving the estimation of soil-available nutrients soil available nutrients estimation at the sub-field scale using time-series UAV observations Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-18 Jihua Meng; Zhiqiang Cheng
Soil-available nutrients (SANs)are essential for crop growth and yield formation. Appropriate variable rate fertilization (VRF) can control SAN at a normal level to avoid unnecessary damage to sustainable production capacity. The precondition of optimizing the application of VRF is obtaining the real-time status of SAN. A new method for SAN estimation has been proposed by integrating modified World
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An unambiguous moving target imaging method for maritime surveillance in high-resolution wide-swath mode Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-18 Xinlin Jia; Hongjun Song; Huaitao Fan; Wenjing He
Multichannel synthetic aperture radar (SAR) is an effective method to achieve high-resolution wide-swath (HRWS) imaging simultaneously. Many algorithms have been proposed to reconstruct stationary targets. However, moving targets cannot be well reconstructed through traditional reconstruct algorithm for static scenes and the ghost targets of the moving targets will have a great influence on SAR image
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Using Siamese capsule networks for remote sensing scene classification Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-18 Song Zhou; Yong Zhou; Bing Liu
The convolutional neural network (CNN) is widely used for image classification because of its powerful feature extraction capability. The key challenge of CNN in remote sensing (RS) scene classification is that the size of data set is small and images in each category vary greatly in position and angle, while the spatial information will be lost in the pooling layers of CNN. Consequently, how to extract
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Are extreme soil moisture deficits captured by remotely sensed data retrievals? Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-18 K. H. Breen; J. D. White; S. C. James
Accurate soil moisture (SM) data are key for climate and surface-atmosphere simulations associated with prediction and analysis of weather and climate variability. Here, we assessed in situ vs. remotely sensed SM discrepancies in a humid watershed during an extreme drought and an arid watershed under peak dry-season conditions. Using in situ SM measurements from the Soil and Climate Analysis Network
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Sentinel-2 based prediction of spruce budworm defoliation using red-edge spectral vegetation indices Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-18 Rajeev Bhattarai; Parinaz Rahimzadeh-Bajgiran; Aaron Weiskittel; David A. MacLean
This research compares the capabilities of various Sentinel-2-derived spectral vegetation indices (SVIs) in particular red-edge SVIs to detect and classify spruce budworm (Choristoneura fumiferana) (SBW) defoliation using Support Vector Machine (SVM) and Random Forest (RF) models. The results showed the superiority of RF in model building for defoliation detection and classification into three classes
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Aircraft detection in remote sensing images using centre-based proposal regions and invariant features Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-18 Huanqian Yan
Aircraft detection in remote sensing imagery has drawn much attention in recent years, which plays an important role in various military and civil applications. While many advanced works have been developed with powerful learning algorithms in natural images, there still lacks an effective one to detect aircraft precisely in remote sensing images, especially in some complicated conditions. In this
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Multispectral image fusion based on map estimation with improved detail Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-18 Arian Azarang; Nasser Kehtarnavaz
This paper presents a multispectral image fusion method for remote sensing applications by using an improved estimation of detail maps. This estimation is achieved by considering a linear combination of the other MultiSpectral (MS) bands as well as their joint multiplications. A comparison with seven existing methods is carried out based on three public domain datasets and by considering commonly used
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Representation of BVMD features via multitask compressive sensing for SAR target classification Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-18 Lin Chen; Peng Zhan; Teng Li; Xueqing Li
This letter develops a synthetic aperture radar (SAR) target classification method based on bidimensional variational mode decomposition (BVMD) and multitask compressive sensing (MTCS). BVMD is employed to decompose SAR images to exploit the time-frequency properties of the described targets. The MTCS is used to jointly classify the original SAR image and its BVMD components. So, the merits of BVMD
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Remote Sensing Letters contribution to the success of the Sustainable Development Goals - UN 2030 agenda Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-18 Costas A. Varotsos; Arthur P. Cracknell
(2020). Remote Sensing Letters contribution to the success of the Sustainable Development Goals - UN 2030 agenda. Remote Sensing Letters: Vol. 11, No. 8, pp. 715-719.
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SAR target classification using multi-aspect multi-feature collaborative representation Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-18 Juan Wang; Xinzheng Zhang; Miaomiao Liu; Xiaoheng Tan
In this paper, a novel approach is proposed for Synthetic Aperture Radar (SAR) target classification based on multi-aspect multi-feature collaborative representation. Firstly, principal component analysis (PCA), wavelet and 2-dimensional slice Zernike moments (2DSZM) features are extracted from SAR images. Next, based on the strong correlation among the adjacent aspect SAR target images, we extend
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An adaptive-trimming-depth based CFAR detector of heterogeneous environment in SAR imagery Remote Sens. Lett. (IF 2.298) Pub Date : 2020-06-18 Jiaqiu Ai; Zhenxiang Cao; Mengdao Xing
An adaptive-trimming-depth-based constant false alarm rate (ATD-CFAR) ship detector of heterogeneous environment in SAR imagery is proposed in this letter. Traditional CFAR detectors generally use all samples in the background window for parameter estimation. However, in the heterogeneous regions, these detectors will overestimate the parameters used for statistical modelling due to the interference
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A deep feature manifold embedding method for hyperspectral image classification Remote Sens. Lett. (IF 2.298) Pub Date : 2020-05-28 Jiamin Liu; Song Yang; Hong Huang; Zhengying Li; Guangyao Shi
In this letter, we proposed a novel deep feature manifold embedding method to improve feature extraction ability of traditional deep learning methods. This method first obtains deep features of hyperspectral image (HSI) from a trained autoencoder. Then, an intrinsic graph and a penalty graph are constructed to discover the discriminant manifold structure of deep features. Finally, the deep features
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Persistent data gap in ocean color observations over the East China Sea in winter: causes and reconstructions Remote Sens. Lett. (IF 2.298) Pub Date : 2020-05-28 Ting Lin; Qingyou He; Weikang Zhan; Haigang Zhan
The instrument used for ocean colour remote-sensing works in the visible wavelengths, and the presence of clouds frequently lead to invalid observations. The formation of clouds is known to be influenced by mesoscale oceanic processes (e.g., eddies and temperature fronts), but these influences are often overlooked in missing data reconstructions. By analysing more than 10 years of chlorophyll-a (chl-a)
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