A Review of Researches on Deep Learning in Remote Sensing Application

International Journal of Geosciences

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ISSN Online: 2156-8367
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"A Review of Researches on Deep Learning in Remote Sensing Application"
written by Ming Zhu, Yongning He, Qingyu He,
published by International Journal of Geosciences, Vol.10 No.1, 2019

has been cited by the following article(s):

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[23]	The application of deep learning for remote sensing of soil organic carbon stocks distribution in South Africa. Ukusetshenziswa kokufunda okujulile kokuzwa …
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[18]	Evaluation of projected soil organic carbon stocks under future climate and land cover changes in South Africa using a deep learning approach Journal of Environmental Management, 2023 DOI:10.1016/j.jenvman.2022.117127

[19]	A distributed land cover classification of FP and CP SAR observation using MapReduce-based multi-layer perceptron algorithm over the Mumbai mangrove region of India International Journal of Remote Sensing, 2023 DOI:10.1080/01431161.2023.2185114

[20]	Harvesting the Landsat archive for land cover land use classification using deep neural networks: Comparison with traditional classifiers and multi-sensor benefits ISPRS Journal of Photogrammetry and Remote Sensing, 2023 DOI:10.1016/j.isprsjprs.2023.05.005

[21]	A Combined Deep Learning and Prior Knowledge Constraint Approach for Large-Scale Forest Disturbance Detection Using Time Series Remote Sensing Data Remote Sensing, 2023 DOI:10.3390/rs15122963

[22]	SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection Applied Sciences, 2023 DOI:10.3390/app13148161

[23]	Remote sensing imagery segmentation in object-based analysis: A review of methods, optimization, and quality evaluation over the past 20 years Remote Sensing Applications: Society and Environment, 2023 DOI:10.1016/j.rsase.2023.101031

[24]	Performance Evaluation Of Various Machine Learning Algorithms For Class Imbalanced Landslides Identification 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3), 2023 DOI:10.1109/IC2E357697.2023.10262453

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[26]	AGs-Unet: Building Extraction Model for High Resolution Remote Sensing Images Based on Attention Gates U Network Sensors, 2022 DOI:10.3390/s22082932

[27]	Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review Sensors, 2022 DOI:10.3390/s22114097

[28]	Generative Feature Extraction From Sentinel 1 and 2 Data for Prediction of Forest Aboveground Biomass in the Italian Alps IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 DOI:10.1109/JSTARS.2022.3179027

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[30]	Comparing OBIA-Generated Labels and Manually Annotated Labels for Semantic Segmentation in Extracting Refugee-Dwelling Footprints Applied Sciences, 2022 DOI:10.3390/app122111226

[31]	Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 DOI:10.1109/JSTARS.2020.3046053

[32]	An Approach to Mapping Deforestation in Permanent Forest Reserve Using the Convolutional Neural Network and Sentinel-1 Synthetic Aperture Radar 2021 Fifth International Conference on Information Retrieval and Knowledge Management (CAMP), 2021 DOI:10.1109/CAMP51653.2021.9498144

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[34]	A deep learning training method of water identification based on the Third National Land Survey data 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021 DOI:10.1117/12.2615106

[35]	Review on remote sensing methods for landslide detection using machine and deep learning Transactions on Emerging Telecommunications Technologies, 2020 DOI:10.1002/ett.3998

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