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Single-shot super-resolution and non-uniformity correction through wavefront modulation in infrared imaging systems J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-03-01 Guillermo Machuca, Pablo Meza, Esteban Vera
Infrared (IR) imaging systems have sensor and optical limitations that result in degraded imagery. Apart from imperfect optics and the finite detector size being responsible for introducing blurring and aliasing, the detector fixed-pattern noise also adds a significant layer of degradation in the collected imagery. Here, we propose a single-shot super-resolution method that compensates for the nonuniformity
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Detection of intrinsic variants of an endmember in hyperspectral images based on local spatial and spectral features J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Gouri Shankar Chetia, Bishnulatpam Pushpa Devi
In recent years, addressing spectral variability in hyperspectral data has improved blind hyperspectral unmixing performance and gained attention in endmember detection applications. Current approaches to address the problem of spectral variability associate the variabilities with the valid endmember and attempt to mitigate the ill-effects caused by them. However, intrinsic variabilities induced by
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ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Xunxun Zhang, Xiaoyu Lu
Vehicle detection using aerial thermal infrared images has received significant attention because of its strong capability for day and night observations to supply information for vehicle tracking, traffic monitoring, and road network planning. Compared with aerial visible images, aerial thermal infrared images are not sensitive to lighting conditions. However, they have low contrast and blurred edges
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Retrieval of land surface temperature from INS-2TD thermal infrared observations using a generalized single-channel algorithm over South-Asia region J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Jalpesh A. Dave, Mehul R. Pandya, Dhiraj B. Shah, Hasmukh K. Varchand, Parthkumar N. Parmar, Himanshu J. Trivedi, Vishal N. Pathak
The experimental Indian Nano-Satellite (INS)-2TD acquires data in a long-wave infrared (7 to 16 μm) region with a fairly good spatial resolution of 175 m. Our study focuses on the retrieval of land surface temperature (LST) using a physics-based generalized single-channel (GSC) algorithm for the INS-2TD observations. A total of 597,240 at-sensor radiance simulations were carried out using moderate
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SMFD: an end-to-end infrared and visible image fusion model based on shared-individual multi-scale feature decomposition J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Mingrui Xu, Jun Kong, Min Jiang, Tianshan Liu
By leveraging the characteristics of different optical sensors, infrared and visible image fusion generates a fused image that combines prominent thermal radiation targets with clear texture details. Existing methods often focus on a single modality or treat two modalities equally, which overlook the distinctive characteristics of each modality and fail to fully utilize their complementary information
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Evaluating gradient descent variations for artificial neural network bathymetry modeling and sensitivity analysis J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Chih-Hung Lee, Min-Kung Hsu, Yu-Min Wang, Jan-Mou Leu, Chung-Ling Chen, Liwei Liu
Artificial intelligence has been widely applied to water depth retrieval across various environments, deemed essential for habitat modeling, hydraulic structure design, and watershed management. However, most of these models have been developed for deep waters, with the critical impact of the gradient descent algorithm often not evaluated. To address this gap in current research, this study adopted
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Deep neural network based on attention and feature complementary fusion for synthetic aperture radar image classification with small samples J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Xiaoning Liu, Furong Shi, Haixia Xu, Liming Yuan, Xianbin Wen
In recent years, methods based on convolutional neural networks (CNNs) have achieved significant results in the problem of target classification of synthetic aperture radar (SAR) images. However, the challenges of SAR image data labeling and the characteristics of CNNs relying on a large amount of labeled data for training have seriously limited the further development of this field. In this work,
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Multi-depth temperature prediction using machine learning for pavement sections J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Yunyan Huang, Mohamad Molavi Nojumi, Shadi Ansari, Leila Hashemian, Alireza Bayat
The temperature of hot mix asphalt (HMA), base, and subgrade layers plays a significant role in pavement performance, because temperature influences the strength of the materials. Therefore, a model to predict temperature throughout the entire pavement structure is desirable. However, most existing models only focus on predicting the temperature of the road surface or the HMA layer, and these models
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Radar high-speed maneuvering weak target detection based on radon dynamic path optimization and fixed point iteration J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Fatao Hou
Long-time coherent integration is known as a powerful method to detect the weak target. However, its effectiveness is limited by the target motion across range and Doppler bins. For the high-speed target, it is highly possible that the range bin crossing (RBC) problem occurs, and for maneuvering target, the Doppler bin crossing (DBC) problem cannot be neglected. In this paper, we propose a Radon dynamic
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Combining multisource remote sensing data to calculate individual tree biomass in complex stands J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Xugang Lian, Hailang Zhang, Leixue Wang, Yulu Gao, Lifan Shi, Yu Li, Jiang Chang
Accurate estimation of forest individual tree characteristics and biomass is very important for monitoring global carbon storage and carbon cycle. In order to solve the problem of calculating individual biomass of various tree species in complex stands, we take terrestrial laser scanning data, unmanned aerial vehicle-laser scanning data, and multispectral data as data sources and extract spectral characteristics
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Use of synthetic aperture radar data for the determination of normalized difference vegetation index and normalized difference water index J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Amazonino Lemos de Castro, Miqueias Lima Duarte, Henrique Ewbank, Roberto Wagner Lourenço
This study was based on analysis of Sentinel-1 (SAR) data to estimate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) during the period 2019 to 2020 in a region with a range of different land uses. The methodology adopted involved the construction of four regression models: linear regression (LR), support vector machine (SVM), random forest (RF), and
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Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methods J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Junjie Luo, Jiao Guo, Zhe Zhu, Yunlong Du, Yongkai Ye
Accurate orchard spatial distribution information can help government departments to formulate scientific and reasonable agricultural economic policies. However, it is prominent to apply remote sensing images to obtain orchard planting structure information. The traditional multidimensional remote sensing data processing, dimension reduction and classification, which are two separate steps, cannot
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Ningaloo eclipse: moon shadow speed and land surface temperature effects from Himawari-9 satellite measurements J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Fred Prata
A total solar eclipse occurred on April 20, 2023, with the umbral shadow touching the Australian continent over the Ningaloo coastal region, near the town of Exmouth, Western Australia. Eclipse totality lasted ∼1 min, reaching totality at ∼03:29 UTC and happened under cloudless skies. Here, we show that the speed of the Moon’s shadow over the land surface can be estimated from 10 min sampling in both
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Unsupervised burned areas detection using multitemporal synthetic aperture radar data J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 José Victor Orlandi Simões, Rogerio Galante Negri, Felipe Nascimento Souza, Tatiana Sussel Gonçalves Mendes, Adriano Bressane
Climate change is a critical concern that has been greatly affected by human activities, resulting in a rise in greenhouse gas emissions. Its effects have far-reaching impacts on both living and non-living components of ecosystems, leading to alarming outcomes such as a surge in the frequency and severity of fires. This paper presents a data-driven framework that unifies time series of remote sensing
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Generation of synthetic generative adversarial network-based multispectral satellite images with improved sharpness J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Lydia Abady, Mauro Barni, Andrea Garzelli, Benedetta Tondi
The generation of synthetic multispectral satellite images has not yet reached the quality level achievable in other domains, such as the generation and manipulation of face images. Part of the difficulty stems from the need to generate consistent data across the entire electromagnetic spectrum covered by such images at radiometric resolutions higher than those typically used in multimedia applications
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Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Quentin Frederick, Megan M. Dewdney, Mark A. Ritenour
Citrus black spot (CBS) is a fungal disease caused by Phyllosticta citricarpa that poses a quarantine threat and can restrict market access to fruits. It manifests as lesions on the fruit surface and can result in premature fruit drops, leading to reduced yield. Another significant disease affecting citrus is canker, which is caused by the bacterium Xanthomonas citri subsp. citri (syn. X. axonopodis
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Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-02-01 Nirmawana Simarmata, Ketut Wikantika, Soni Darmawan, Agung Budi Harto, Anjar Dimara Sakti, Aki Asmoro Santo
Mangroves maintain coastal balance and have the greatest potential for carbon sequestration. Most mapping studies on mangroves have focused on their extent and distribution and rarely featured mangrove species. Therefore, the objective of our study is to investigate mangrove species mapping from integrated Sentinel-2 imagery and field spectral data using a random forest (RF) algorithm. Study areas
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Extraction of pine wilt disease based on a two-stage unmanned aerial vehicle deep learning method J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Xin Huang, Weilin Gang, Jiayi Li, Zhili Wang, Qun Wang, Yuegang Liang
Forestry pests pose a significant threat to forest health, making precise extraction of infested trees a vital aspect of forest protection. In recent years, deep learning has achieved substantial success in detecting infestations. However, when applying existing deep learning methods to infested tree detection, challenges arise, such as limited training samples and confusion between forest areas and
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Multi-scale contrastive learning method for PolSAR image classification J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Wenqiang Hua, Chen Wang, Nan Sun, Lin Liu
Although deep learning-based methods have made remarkable achievements in polarimetric synthetic aperture radar (PolSAR) image classification, these methods require a large number of labeled samples. However, for PolSAR image classification, it is difficult to obtain a large number of labeled samples, which requires extensive human labor and material resources. Therefore, a new PolSAR image classification
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Plume motion characterization in unmanned aerial vehicle aerial video and imagery J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Mehrube Mehrubeoglu, Kirk Cammarata, Hua Zhang, Lifford McLauchlan
Sediment plumes are generated from both natural and human activities in benthic environments, increasing the turbidity of the water and reducing the amount of sunlight reaching the benthic vegetation. Seagrasses, which are photosynthetic bioindicators of their environment, are threatened by chronic reductions in sunlight, impacting entire aquatic food chains. Our research uses unmanned aerial vehicle
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Synthetic aperture radar image change detection using saliency detection and attention capsule network J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Shaona Wang, Di Wang, Jia Shi, Zhenghua Zhang, Xiang Li, Yanmiao Guo
Synthetic aperture radar (SAR) image change detection has been widely applied in a variety of fields as one of the research hotspots in remote sensing image processing. To increase the accuracy of SAR image change detection, an algorithm based on saliency detection and an attention capsule network is proposed. First, the difference image (DI) is processed using the saliency detection method. The DI’s
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CBTA: a CNN-BiGRU method with triple attention for winter wheat yield prediction J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Wenzheng Ye, Tinghuai Ma, Zilong Jin, Huan Rong, Benjamin Kwapong Osibo, Mohamed Magdy Abdel Wahab, Yuming Su, Bright Bediako-Kyeremeh
Timely and accurate prediction of winter wheat yield contributes to ensuring national food security. We propose a CNN- bidirectional gated recurrent unit method with triple attention for winter wheat yield prediction, named CBTA. This deep learning model uses convolutional neural networks to mine the spatial spectral information in hyperspectral remote sensing images. Furthermore, the bidirectional
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Monitoring of land subsidence by combining small baseline subset interferometric synthetic aperture radar and generic atmospheric correction online service in Qingdao City, China J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Xuepeng Li, Qiuxiang Tao, Yang Chen, Anye Hou, Ruixiang Liu, Yixin Xiao
Owing to accelerated urbanization, land subsidence has damaged urban infrastructure and impeded sustainable economic and social development in Qingdao City, China. Combining interferometric synthetic aperture radar (InSAR) and generic atmospheric correction online service (GACOS), atmospheric correction has not yet been investigated for land subsidence in Qingdao. A small baseline subset of InSAR (SBAS
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Man-made object segmentation around reservoirs by an end-to-end two-phase deep learning-based workflow J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Nayereh Hamidishad, Roberto Marcondes Cesar Jr.
Reservoirs are fundamental infrastructures for the management of water resources. Constructions around them can negatively impact their water quality. Such constructions can be detected by segmenting man-made objects around reservoirs in the remote sensing (RS) images. Deep learning (DL) has attracted considerable attention in recent years as a method for segmenting the RS imagery into different land
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Continual domain adaptation on aerial images under gradually degrading weather J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Chowdhury Sadman Jahan, Andreas Savakis
Domain adaptation (DA) aims to reduce the effects of the distribution gap between the source domain where a model is trained and the target domain where the model is deployed. When a deep learning model is deployed on an aerial platform, it may face gradually degrading weather conditions during its operation, leading to gradually widening gaps between the source training data and the encountered target
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Multiscale graph convolution residual network for hyperspectral image classification J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Ao Li, Yuegong Sun, Cong Feng, Yuan Cheng, Liang Xi
In recent years, graph convolutional networks (GCNs) have attracted increased attention in hyperspectral image (HSI) classification through the utilization of data and their connection graph. However, most existing GCN-based methods have two main drawbacks. First, the constructed graph with pixel-level nodes loses many useful spatial information while high computational cost is required due to large
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Feasibility of remote estimation of optical turbulence via quick response code imaging J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Burton Neuner III, Skylar D. Lilledahl, Kyle R. Drexler
Turbulence estimation theory is presented and demonstrated by imaging a series of spatially encoded quick response (QR) codes in ambient radiation through atmospheric scintillation. This remote sensing concept was verified though preliminary feasibility experiments and detailed MATLAB simulations using QR codes displayed on a low-power digital e-ink screen. Of note, knowledge of propagation range and
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EPAWFusion: multimodal fusion for 3D object detection based on enhanced points and adaptive weights J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Xiang Sun, Shaojing Song, Fan Wu, Tingting Lu, Bohao Li, Zhiqing Miao
Fusing LiDAR point cloud and camera image for 3D object detection in autonomous driving has emerged as a captivating research avenue. The core challenge of multimodal fusion is how to seamlessly fuse 3D LiDAR point cloud with 2D camera image. Although current approaches exhibit promising results, they often rely solely on fusion at either the data level, feature level, or object level, and there is
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Spatiotemporal fusion convolutional neural network: tropical cyclone intensity estimation from multisource remote sensing images J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Randi Fu, Haiyan Hu, Nan Wu, Zhening Liu, Wei Jin
Utilizing multisource remote sensing images to accurately estimate tropical cyclone (TC) intensity is crucial and challenging. Traditional approaches rely on a single image for intensity estimation and lack the capability to perceive dynamic spatiotemporal information. Meanwhile, many existing deep learning methods sample from a time series of fixed length and depend on computation-intensive 3D feature
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Exploring impacts of aerosol on convective clouds using satellite remote sensing and machine learning J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Jiaqin Mi, Yuanjian Yang, Shuxue Zhou, Xiaoyan Ma, Siying Wei
Aerosol–cloud–precipitation interaction is currently a research hotspot that is challenging but also one of the most prominent sources of uncertainty affecting climate change. We have identified 1082 mesoscale convective systems (MCSs) over eastern China from April to September in 2016 and 2017. Overall, the occurrence frequency and MCS area increased when altitude increased, as demonstrated by the
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Segmentation-based VHR SAR images built-up area change detection: a coarse-to-fine approach J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Jingxing Zhu, Feng Wang, Hongjian You
The change detection in built-up areas within very high resolution synthetic aperture radar images is a very challenging task due to speckle noise and geometric distortions caused by the unique imaging mechanism. To tackle this issue, we propose an object-based coarse-to-fine change detection method that integrates segmentation and uncertainty analysis techniques. First, we propose a multi-temporal
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2023 List of Reviewers J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01
JARS thanks the reviewers who served the journal in 2023.
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LRSNet: a high-efficiency lightweight model for object detection in remote sensing J. Appl. Remote Sens. (IF 1.7) Pub Date : 2024-01-01 Shiliang Zhu, Min Miao, Yutong Wang
Unmanned aerial vehicles (UAVs) exhibit the ability to flexibly conduct aerial remote-sensing imaging. By employing deep learning object-detection algorithms, they efficiently perceive objects, finding widespread application in various practical engineering tasks. Consequently, UAV-based remote sensing object detection technology holds considerable research value. However, the background of UAV remote
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Loaded waveguide measurements of plastic explosives at V-band J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-12-01 Zachary J. Landicini, Jeffrey Barber, James C. Weatherall, Duane C. Karns, Peter R. Smith, Joaquín Aparicio-Bolaño, Wendy Ruiz
Dielectric measurements of plastic explosives using a loaded waveguide technique via vector network analyzer and banded millimeter wave extender modules operating at V-band (50 to 75 GHz) are performed. A portion of an explosive sample is inserted into a waveguide shim 2 mm in length and trimmed flush with the faces of the shim. Two-port S-parameter measurements are conducted on the explosive; the
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Special Section Guest Editorial: Integrating Remote Sensing, Machine Learning, and Data Science for Air Quality Management J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-12-01 Kaixu Bai, Simone Lolli, Yuanjian Yang
Guest editors Kaixu Bai, Simone Lolli, and Yuanjian Yang introduce the Special Section on Integrating Remote Sensing, Machine Learning, and Data Science for Air Quality Management.
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M2-APNet: A multimodal deep learning network to predict major air pollutants from temporal satellite images J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-11-01 Gudiseva Swetha, Rajeshreddy Datla, Chalavadi Vishnu, C. Krishna Mohan
Air quality monitoring plays a vital role in the sustainable development of any country. Continuous monitoring of the major air pollutants and forecasting their variations would be helpful in saving the environment and improving the quality of public health. However, this task becomes challenging with the available observations of air pollutants from the on-ground instruments with their limited spatial
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Comparative evaluation of backpropagation neural network and genetic algorithm-backpropagation neural network models for PM2.5 concentration prediction based on aerosol optical depth, meteorological factors, and air pollutants J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-10-01 Jilin Gu, Shuang Liang, Qiao Song, Yuwei Li, Yiwei Wang, Shumin Guo
Fine particles with an aerodynamic diameter ≤2.5 μm are called PM2.5, and accurate prediction of PM2.5 concentration can help prevent the harmful effects of heavy pollution on humans. At present, the distribution of ground-based PM2.5 monitoring stations in China’s cities is relatively sparse. Hence, the aerosol optical depth (AOD) obtained from satellite remote sensing provides an effective means
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Post-launch evaluation and improvements of National Oceanic and Atmospheric Administration-20 Visible-Infrared Imaging Radiometer Suite sensor data record geolocation accuracy J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Slawomir Blonski, Wenhui Wang, Khalil Ahmad, Changyong Cao
National Oceanic and Atmospheric Administration (NOAA)-20 (formerly Joint Polar Satellite System-1) is a polar-orbiting weather satellite launched on November 18, 2017. Visible-Infrared Imaging Radiometer Suite (VIIRS) is one of five instruments onboard NOAA-20 (N20), and it jointed on orbit the previous VIIRS instrument operating on the Suomi NPP (SNPP) spacecraft since November 2011. During post-launch
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Comparative analysis of deep learning-based pansharpening methods for improved image classification accuracy J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Volkan Yilmaz, Deryanur Asikoglu
Pansharpened images are frequently utilized as base data in image classification applications. Nonetheless, the accuracy of image classification heavily relies on the efficiency of the pansharpening strategy applied. With numerous existing pansharpening approaches available, it becomes challenging for analysts to select the one that yields the best outcome. Recently, the deep learning (DL)-based pansharpening
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LiDAR technology and experimental research for comprehensive measurement of atmospheric transmittance, turbulence, and wind J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Hao Yang, Duoyang Qiu, Zhiyuan Fang, Yalin Hu, Fei Ming
Atmospheric transmittance, turbulence, and wind play a crucial role in the field of laser atmospheric transmission. In response to the demand for comprehensive detection of atmospheric optical parameters, a LiDAR system for comprehensive measurement of atmospheric transmittance, turbulence, and wind (ACW-LiDAR) has been developed through integrated optical and mechanical design. The remote sensing
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Assessment of personal exposure using movement trajectory and hourly 1-km PM2.5 concentrations J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Heming Bai, Junjie Song, Huiqun Wu, Rusha Yan, Wenkang Gao, Muhammad Jawad Hussain
Most health studies have used residential addresses to assess personal exposure to air pollution. These exposure assessments may suffer from bias due to not considering individual movement. Here, we collected 45,600 hourly movement trajectory data points for 185 individuals in Nanjing from COVID-19 epidemiological surveys. We developed a fusion algorithm to produce hourly 1-km PM2.5 concentrations
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Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Mingliang Ma, Mengnan Liu, Mengjiao Liu, Ke Li, Huaqiao Xing, Fei Meng
It is of great significance to explore the spatial-temporal variations and estimate the relative importance of the influencing factors of PM2.5 and O3 pollution. The study established nationwide surface O3, NO2, and SO2 estimation models using the extreme gradient boosting model and the data fusion method. The cross-validation results indicated that the forecasted models performed well (R-values from
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Automatic registration method for medium-resolution remote sensing images of coral reefs with morphological information pairing and constrained iterative fining J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Zhenying Chen, Yuzhe Pian, Zhenjie Chen, Liang Cheng
Automatic registration of medium-resolution remote sensing images of coral reefs, particularly those without artificial facilities, faces two challenges: difficulty in identifying the same coral reefs in different images and instability of the fine-tuning process. To overcome these challenges, we propose an automatic registration method that combines morphological information pairing with constrained
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Multi-scale nonlinear edge-based three-phase model for unsupervised hyperspectral feature extraction J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Xianyue Wang, Longxia Qian, Chengzu Bai, Jinde Cao
Unsupervised feature extraction techniques of hyperspectral images (HSIs) have recently drawn significant attention for their excellent performance and efficiency in classification. In some existing methods, the denoising process that reduces the influence of inherent noise is ignored, and the nonlinear edge characteristics and multi-scale features that help to classify still need to be fully considered
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Grid and homogeneity-based ground segmentation using light detection and ranging three-dimensional point cloud J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Ciyun Lin, Jie Yang, Bowen Gong, Hongchao Liu, Ganghao Sun
Ground point identification and segmentation are fundamental to the light detection and ranging (LiDAR) based environment perception because they affect the accuracy and computational efficiency in the following data processing steps. A common problem that results in over- and under-segmentation occurs when the objects of interest are nonhomogeneous, and the sampling density is uneven. This study addresses
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Evaluation of systematic errors on polarization parameters from POLDER instrument data for use in CLARREO Pathfinder-VIIRS intercalibration J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Daniel Goldin, Rajendra Bhatt, Yolanda Shea
One of the Climate Absolute Radiance and Refractivity Observatory Pathfinder (CPF) mission’s science objectives is to intercalibrate the reflective solar bands of the NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) instrument against high-accuracy CPF measurements utilizing coincident, co-angled, and co-located footprints acquired over diverse Earth targets. To alleviate the effect of high
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Quantifying the influence of design and location on the cool island effect of the urban parks of Barcelona J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Alan García-Haro, Blanca Arellano, Josep Roca
The aim of this research is to analyze the influence of park design and location on their cooling effect during summer daytime in Barcelona. Spatial analytical methods, utilizing the land surface temperature data from the Landsat 8 satellite, were employed to assess the intensity and spatial extent of the Park Cool Island (PCI) in 86 parks over four consecutive years. The study investigated the influence
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Special Section Guest Editorial: Frontiers in Image and Signal Processing for Remote Sensing J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Chi Lin, Chang Wu Yu
Guest Editors Chi Lin and Chang Wu Yu introduce the Special Section on Frontiers in Image and Signal Processing for Remote Sensing.
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Study of the improvement of the multifractal spatial downscaling by the random forest regression model considering spatial heterogeneity J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Wei Zhang, Chenjia Ji, Shengjie Zheng, Hugo A. Loáiciga, Wenkai Li, Xiaona Sun
Regional hydrological analysis generally requires meteorological inputs with adequate spatial resolution and coverage. Satellite-derived precipitation covers relatively large areas at various temporal scales. The global precipitation measurement (GPM) began releasing a new generation of global precipitation products in April, 2014, i.e., the integrated multi-satellite retrievals for GPM (IMERG), which
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Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Nan Wang, Yuetian Shi, Yinzhu Cheng, Fanchao Yang, Geng Zhang, Siyuan Li, Xuebin Liu
Hyperspectral anomaly detection (HAD) is a technique to find observations without prior knowledge, which is of particular interest as a branch of remote sensing object detection. However, the application of HAD is limited by various challenges, such as high-dimensional data, high intraclass variability, redundant information, and limited samples. To overcome these restrictions, we report an unsupervised
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HyperBlend leaf simulator: improvements on simulation speed, generalizability, and parameterization J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Kimmo A. Riihiaho, Leevi Lind, Ilkka Pölönen
In recent decades, remote sensing of vegetation by hyperspectral imaging has been of great interest. An important part in interpreting the remotely sensed spectral data is played by simulators, which approximate the connection between plants’ biophysical and biochemical properties and detected spectral response. We introduce improvements and new features to recently published hyperspectral leaf model
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U-shaped contourlet network for high-spatial-resolution remote sensing images segmentation J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Weiheng Zhao, Jiannong Cao, Xueyan Dong
Accurate semantic segmentation of images has long been a research priority in remote sensing. However, the presence of geometrically complex and spatially diverse objects increases the difficulty in simultaneously obtaining coherent and accurate labeling result. To solve this challenge, our study combined multiscale geometric feature extraction with convolutional neural network and proposed a new U-shaped
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Learning an ensemble dehazing network for visible remote sensing images J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Yufeng Li, Jiyang Lu, Zhentao Fan, Xiang Chen
Image dehazing is an important preprocessing task since haze extremely degrades the image quality and hampers the application of remote sensing vision system. Although the deep learning-based method has been successful in image dehazing, there has been little effort to harmonize convolutional neural networks and transformer to better satisfy removing haze. In particular, local and global representation
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Settlement monitoring data fusion approach for high-speed railways based on GNSS and InSAR J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Dongwei Qiu, Yuzheng Wang, Yunlong Zhang, Yuci Tong, Haorong Liang, Ke-liang Ding, Shanshan Wan, Jing Wang
The high-precision and high spatio-temporal resolution settlement time series data generated by integrating Global Navigation Satellite System (GNSS) and Interferometric Synthetic-Aperture Radar (InSAR) data are of great value for the safe operation of high-speed railways. The GNSS monitoring stations and InSAR monitoring area present zonal distribution along the high-speed railways, which causes the
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Interferometry modeling and model-based height estimation for buildings in urban DSM reconstruction based on interferometric synthetic aperture radar technology J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-09-01 Di Zhuang, Lamei Zhang, Bin Zou
Benefiting from the advantage of weather and illumination independence, less processing data and shorter data time span, spaceborne interferometric synthetic aperture radar technology has certain potential in building 3D reconstruction and urban DSM reconstruction because of its sensitivity to height. However, the phase mixture in the building layover area makes it difficult to realize the accurate
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Comparative assessment of the accuracies of daytime land surface temperature retrieval methods using Landsat 8 and MODIS imageries in Benin, West Africa J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-08-01 Vignon Adelphe Rosos Djikpo, Oscar Teka, Fortuné Azihou, Ismaïla Toko, Madjidou Oumorou, Brice Sinsin
Land surface temperature (LST) is an important climate variable used to assess the effects of climate change. This research project aims to compare the results of mono-window (MW) and split-window (SW) algorithms against the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 Collection 2 Level-2 surface temperature (L8-C2L2) products and identify the most suitable techniques. In-situ
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Aqua MODIS: 20 years of on-orbit calibration and performance J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-08-01 Xiaoxiong Xiong, Amit Angal, Tiejun Chang, Emily Aldoretta, Xu Geng, Daniel Link, Junqiang Sun, Kevin Twedt, Aisheng Wu
Since its launch in May, 2002, Aqua MODIS has successfully operated for more than 20 years and has continuously generated a wide range of data products that have enabled and supported the remote sensing community and users worldwide for their studies of the Earth’s system by monitoring changes in its key environmental parameters. Although Aqua MODIS, designed with a lifetime requirement of 6 years
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Simple linear iterative clustering and ConvNeXt for mapping vectorize tree species J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-08-01 Ni Wang, Pu Tao, Taisheng Chen
We propose a pioneering approach for gathering data on the forest canopy, one that merges two cutting-edge technologies: ConvNext tiny and simple linear iterative clustering (SLIC) (ConvNeXt and SLIC vectorize for tree species mapping, CSVTSM). By leveraging the clustering label generated by SLIC, CSVTSM obtains the vectorized result of land cover and allows us to obtain the location and distribution
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Gaussian manifold metric learning for hyperspectral image dimensionality reduction and classification J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-08-01 Zhi Xu, Zelin Jiang, Longyang Zhao, Shu Li, Qi Liu
Dimensionality reduction techniques can remove redundant information from hyperspectral images (HSIs) and improve discriminability. However, due to the inherent nonlinear characteristics of HSI, there may be non-Euclidean structures in the data and its topological properties may make it suboptimal to recover the low-dimensional manifolds by means of a linear projection. As a result, linear projection
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Synthetic aperture radar and optical image registration using local and global feature learning by modality-shared attention network J. Appl. Remote Sens. (IF 1.7) Pub Date : 2023-08-01 Xin Hu, Yan Wu, Zhikang Li, Xiaoru Zhao, Xingyu Liu, Ming Li
The registration of synthetic aperture radar (SAR) and optical images is a meaningful but challenging multimodal task. Due to the large radiometric differences between SAR and optical images, it is difficult to obtain discriminative features only by mining local features in the traditional Siamese convolutional networks. We propose a modality-shared attention network (MSA-Net) that introduces nonlocal