A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities

[1]  Dong Liang,et al.  Mapping mountain glaciers using an improved U-Net model with cSE , 2022, Int. J. Digit. Earth.

[2]  R. He,et al.  Towards lithology mapping in semi-arid areas using time-series Landsat-8 data , 2022, Ore Geology Reviews.

[3]  Shengte Wang,et al.  Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark , 2022, Remote. Sens..

[4]  J. Watts,et al.  Local Scale (3-m) Soil Moisture Mapping Using SMAP and Planet SuperDove , 2022, Remote. Sens..

[5]  Jun Yu Li,et al.  A context-scale-aware detector and a new benchmark for remote sensing small weak object detection in unmanned aerial vehicle images , 2022, International Journal of Applied Earth Observation and Geoinformation.

[6]  Cheng Zhong,et al.  Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model , 2022, Sensors.

[7]  S. Lacasse,et al.  Machine learning and landslide studies: recent advances and applications , 2022, Natural Hazards.

[8]  Rajneesh Sharma,et al.  Remote Sensing of Surface and Subsurface Soil Organic Carbon in Tidal Wetlands: A Review and Ideas for Future Research , 2022, Remote. Sens..

[9]  Liang-pei Zhang,et al.  Coupling Model- and Data-Driven Methods for Remote Sensing Image Restoration and Fusion: Improving physical interpretability , 2022, IEEE Geoscience and Remote Sensing Magazine.

[10]  C. Farquharson,et al.  A Random Forest approach to predict geology from geophysics in the Pontiac subprovince, Canada , 2022, Canadian Journal of Earth Sciences.

[11]  D. I. Rukhovich,et al.  Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data , 2022, Remote. Sens..

[12]  Yansheng Li,et al.  Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives , 2022, ISPRS Journal of Photogrammetry and Remote Sensing.

[13]  Xiaobing Zhou,et al.  The Potential of Sentinel-1A Data for Identification of Debris-Covered Alpine Glacier Based on Machine Learning Approach , 2022, Remote. Sens..

[14]  P. Atkinson,et al.  Geoscience-aware deep learning: A new paradigm for remote sensing , 2022, Science of Remote Sensing.

[15]  B. Pradhan,et al.  A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data , 2022, Remote. Sens..

[16]  M. El Ghorfi,et al.  Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach , 2022, Earth Science Informatics.

[17]  Yansheng Li,et al.  Combining deep learning and ontology reasoning for remote sensing image semantic segmentation , 2022, Knowl. Based Syst..

[18]  Pedram Ghamisi,et al.  NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Zama Eric Mashimbye,et al.  Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning , 2021, Remote. Sens..

[20]  A. Shebl,et al.  Lithological mapping enhancement by integrating Sentinel 2 and gamma-ray data utilizing support vector machine: A case study from Egypt , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[21]  Muhammad Imran Malik,et al.  UCL: Unsupervised Curriculum Learning for water body classification from remote sensing imagery , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Chao Wang,et al.  Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR , 2021, Remote. Sens..

[23]  Zhengjing Ma,et al.  Deep learning for geological hazards analysis: Data, models, applications, and opportunities , 2021, Earth-Science Reviews.

[24]  Aimin Li,et al.  Comparative Analysis of Machine Learning Algorithms in Automatic Identification and Extraction of Water Boundaries , 2021, Applied Sciences.

[25]  Zhengyan Wang,et al.  Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network , 2021, PloS one.

[26]  Kishor P. Upla,et al.  A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network , 2021, Multim. Tools Appl..

[27]  H. Tanyaş,et al.  Supplementary material to "Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories" , 2021, Natural Hazards and Earth System Sciences.

[28]  Yihua Tan,et al.  Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero-shot remote sensing image scene classification , 2021 .

[29]  Ke Wu,et al.  Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks , 2021, Remote. Sens..

[30]  Yansheng Li,et al.  MSResNet: Multiscale Residual Network via Self-Supervised Learning for Water-Body Detection in Remote Sensing Imagery , 2021, Remote. Sens..

[31]  Youhei Kawamura,et al.  Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems , 2021, Minerals.

[32]  Pece V. Gorsevski,et al.  Machine learning and multi-sensor data fusion for mapping lithology: a case study of Kowli-kosh area, SW Iran , 2021, Advances in Space Research.

[33]  Lizhe Wang,et al.  Geographic Optimal Transport for Heterogeneous Data: Fusing Remote Sensing and Social Media , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[34]  L. Denis,et al.  Image Restoration for Remote Sensing: Overview and toolbox , 2021, IEEE Geoscience and Remote Sensing Magazine.

[35]  Lizhe Wang,et al.  CycleGAN-STF: Spatiotemporal Fusion via CycleGAN-Based Image Generation , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Sima Peyghambari,et al.  Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review , 2021, Journal of Applied Remote Sensing.

[37]  Iman Salehi Hikouei,et al.  Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments , 2021, Sensors.

[38]  Dan-Yu Qiao,et al.  Fusion of China ZY-1 02D Hyperspectral Data and Multispectral Data: Which Methods Should Be Used? , 2021, Remote. Sens..

[39]  Anima Anandkumar,et al.  SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers , 2021, NeurIPS.

[40]  Sajid Siraj,et al.  An Overview of Multi-Criteria Decision Analysis (MCDA) Application in Managing Water-Related Disaster Events: Analyzing 20 Years of Literature for Flood and Drought Events , 2021, Water.

[41]  Cordelia Schmid,et al.  Segmenter: Transformer for Semantic Segmentation , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[42]  Hongyan He,et al.  Reconstructing coastal blue with blue spectrum based on ZY-1(02D) satellite , 2021 .

[43]  Ari S. Morcos,et al.  ConViT: improving vision transformers with soft convolutional inductive biases , 2021, ICML.

[44]  Ehsan Farahbakhsh,et al.  A review of machine learning in processing remote sensing data for mineral exploration , 2021, Remote Sensing of Environment.

[45]  E. Zhang,et al.  An automated, generalized, deep-learning-based method for delineating the calving fronts of Greenland glaciers from multi-sensor remote sensing imagery , 2021 .

[46]  Lizhe Wang,et al.  A Survey on Active Deep Learning: From Model-driven to Data-driven , 2021, 2101.09933.

[47]  Jining Yan,et al.  Methods for Small, Weak Object Detection in Optical High-Resolution Remote Sensing Images: A survey of advances and challenges , 2021, IEEE Geoscience and Remote Sensing Magazine.

[48]  D. I. Rukhovich,et al.  The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution , 2021, Remote. Sens..

[49]  Xiaodao Chen,et al.  Improving Training Instance Quality in Aerial Image Object Detection With a Sampling-Balance-Based Multistage Network , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[50]  T. Bolch,et al.  Automated detection of rock glaciers using deep learning and object-based image analysis , 2020 .

[51]  Datong Liu,et al.  Real-Time Fault Detection for UAV Based on Model Acceleration Engine , 2020, IEEE Transactions on Instrumentation and Measurement.

[52]  M. Cracknell,et al.  Identification of intrusive lithologies in volcanic terrains in British Columbia by machine learning using random forests: The value of using a soft classifier , 2020 .

[53]  Lizhe Wang,et al.  High-Resolution Remote Sensing Image Scene Classification via Key Filter Bank Based on Convolutional Neural Network , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[54]  Ian Goodfellow,et al.  Generative adversarial networks , 2020, Commun. ACM.

[55]  Jing Cheng,et al.  Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning , 2020, Earth Sciences Research Journal.

[56]  Yu Chen,et al.  RETRACTED ARTICLE: A novel approach for scene classification from remote sensing images using deep learning methods , 2020, European Journal of Remote Sensing.

[57]  Duccio Rocchini,et al.  Integration of hyperspectral and LiDAR data for mapping small water bodies , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[58]  Lizhe Wang,et al.  Sample generation based on a supervised Wasserstein Generative Adversarial Network for high-resolution remote-sensing scene classification , 2020, Inf. Sci..

[59]  Pascal Audet,et al.  Automatic Detection and Location of Seismic Events From Time‐Delay Projection Mapping and Neural Network Classification , 2020, Journal of Geophysical Research: Solid Earth.

[60]  Thomas Blaschke,et al.  Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria , 2020, Remote. Sens..

[61]  Wentao Yang,et al.  Automatic Mapping of Landslides by the ResU-Net , 2020, Remote. Sens..

[62]  Naonori Ueda,et al.  Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data , 2020, Remote. Sens..

[63]  Hongzhang Xu,et al.  Deep learning in environmental remote sensing: Achievements and challenges , 2020, Remote Sensing of Environment.

[64]  Xilin Chen,et al.  Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[65]  Snehamoy Chatterjee,et al.  Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti, India , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[66]  Zhaopeng Xu,et al.  Radiometric Cross-Calibration of the Wide Field View Camera Onboard GaoFen-6 in Multispectral Bands , 2020, Remote. Sens..

[67]  Lifei Wei,et al.  A robust spectral-spatial approach to identifying heterogeneous crops using remote sensing imagery with high spectral and spatial resolutions , 2020 .

[68]  Mark Crowley,et al.  A review of machine learning applications in wildfire science and management , 2020, Environmental Reviews.

[69]  Yi Wang,et al.  Flood susceptibility mapping using convolutional neural network frameworks , 2020 .

[70]  N. Chen,et al.  Glacial Lake Inventory and Lake Outburst Flood/Debris Flow Hazard Assessment after the Gorkha Earthquake in the Bhote Koshi Basin , 2020 .

[71]  Zeyu Liu,et al.  Intelligent High-Resolution Geological Mapping Based on SLIC-CNN , 2020, ISPRS Int. J. Geo Inf..

[72]  Lorenzo Bruzzone,et al.  Hyperspectral Band Selection for Lithologic Discrimination and Geological Mapping , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[73]  Yu Wang,et al.  Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. , 2020, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[74]  Ying Zhang,et al.  The Advanced Hyperspectral Imager: Aboard China's GaoFen-5 Satellite , 2019, IEEE Geoscience and Remote Sensing Magazine.

[75]  Yonghong Zhang,et al.  Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China , 2019, Remote. Sens..

[76]  Jonathan Li,et al.  Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[77]  Demis Hassabis,et al.  Mastering Atari, Go, chess and shogi by planning with a learned model , 2019, Nature.

[78]  Celia A. Baumhoer,et al.  Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning , 2019, Remote. Sens..

[79]  Heike K. Lotze,et al.  Eelgrass (Zostera marina) and benthic habitat mapping in Atlantic Canada using high-resolution SPOT 6/7 satellite imagery , 2019, Estuarine, Coastal and Shelf Science.

[80]  G. Asner,et al.  Adaptive bathymetry estimation for shallow coastal waters using Planet Dove satellites , 2019, Remote Sensing of Environment.

[81]  David R. Thompson,et al.  Spectral and Radiometric Calibration of the Next Generation Airborne Visible Infrared Spectrometer (AVIRIS-NG) , 2019, Remote. Sens..

[82]  Zine El Abidine El Morjani,et al.  Use of the Sentinel-2A Multispectral Image for Litho-Structural and Alteration Mapping in Al Glo’a Map Sheet (1/50,000) (Bou Azzer–El Graara Inlier, Central Anti-Atlas, Morocco) , 2019, Artificial Satellites.

[83]  Kaiyong Zhao,et al.  AutoML: A Survey of the State-of-the-Art , 2019, Knowl. Based Syst..

[84]  Sanja Fidler,et al.  Gated-SCNN: Gated Shape CNNs for Semantic Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[85]  David E. Knapp,et al.  Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites , 2019, Remote. Sens..

[86]  Yasushi Yamaguchi,et al.  Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration , 2019, Remote. Sens..

[87]  Massimo Menenti,et al.  Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water , 2019, Remote. Sens..

[88]  W. Brian Whalley,et al.  Rock glaciers and mountain hydrology: A review , 2019, Earth-Science Reviews.

[89]  Yi Wang,et al.  Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. , 2019, The Science of the total environment.

[90]  G. Jia,et al.  Improving meteorological drought monitoring capability over tropical and subtropical water-limited ecosystems: evaluation and ensemble of the Microwave Integrated Drought Index , 2019, Environmental Research Letters.

[91]  Taghi M. Khoshgoftaar,et al.  Survey on deep learning with class imbalance , 2019, J. Big Data.

[92]  Xin Jing,et al.  Evaluation of RadCalNet Output Data Using Landsat 7, Landsat 8, Sentinel 2A, and Sentinel 2B Sensors , 2019, Remote. Sens..

[93]  Wei Wu,et al.  Investigation of Remote Sensing Imageries for Identifying Soil Texture Classes Using Classification Methods , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[94]  Bin Chen,et al.  Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. , 2019, Science bulletin.

[95]  Thomas Blaschke,et al.  Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection , 2019, Remote. Sens..

[96]  Gongzhuang Peng,et al.  Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway , 2018, Sensors.

[97]  Chunming Wu,et al.  A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data , 2018, J. Circuits Syst. Comput..

[98]  Chiman Kwan,et al.  Deep Learning with Synthetic Hyperspectral Images for Improved Soil Detection in Multispectral Imagery , 2018, 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[99]  Wei Wu,et al.  Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water , 2018, Remote. Sens..

[100]  D. H. Roberts,et al.  Glacial geomorphological mapping: A review of approaches and frameworks for best practice , 2018, Earth-Science Reviews.

[101]  Mingjie Sun,et al.  Rethinking the Value of Network Pruning , 2018, ICLR.

[102]  Mei-Ling Shyu,et al.  A Survey on Deep Learning , 2018, ACM Comput. Surv..

[103]  Jun Fu,et al.  Dual Attention Network for Scene Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[104]  Alex Sherstinsky,et al.  Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network , 2018, Physica D: Nonlinear Phenomena.

[105]  Matthew J. Cracknell,et al.  Geological Mapping in Western Tasmania Using Radar and Random Forests , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[106]  Nikos Komodakis,et al.  Cloud-Gan: Cloud Removal for Sentinel-2 Imagery Using a Cyclic Consistent Generative Adversarial Networks , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[107]  Shiqiang Zhang,et al.  Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review , 2018, Reviews of Geophysics.

[108]  Juha Hyyppä,et al.  A review: remote sensing sensors , 2018 .

[109]  Guangxing Wang,et al.  Prediction of soil properties using a hyperspectral remote sensing method , 2018 .

[110]  Matthew J. Cracknell,et al.  Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia , 2018, GEOPHYSICS.

[111]  Ahmed El-Rabbany,et al.  Using multispectral airborne LiDAR data for land/water discrimination: a case study at Lake Ontario, Canada , 2018 .

[112]  R. Betts,et al.  Mountain rock glaciers contain globally significant water stores , 2018, Scientific Reports.

[113]  Menglong Yan,et al.  Automatic Water-Body Segmentation From High-Resolution Satellite Images via Deep Networks , 2018, IEEE Geoscience and Remote Sensing Letters.

[114]  George Papandreou,et al.  Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.

[115]  Xin Xu,et al.  Gaofen-3 PolSAR Image Classification via XGBoost and Polarimetric Spatial Information , 2018, Sensors.

[116]  Huazhong Ren,et al.  Improving Land Surface Temperature and Emissivity Retrieval From the Chinese Gaofen-5 Satellite Using a Hybrid Algorithm , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[117]  Jingwei Wu,et al.  Comparison of partial least square regression, support vector machine, and deep-learning techniques for estimating soil salinity from hyperspectral data , 2018 .

[118]  Arnt-Børre Salberg,et al.  Tree species classification in Norway from airborne hyperspectral and airborne laser scanning data , 2018 .

[119]  Lizhe Wang,et al.  A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.

[120]  Katsuaki Koike,et al.  Transformation of Landsat imagery into pseudo-hyperspectral imagery by a multiple regression-based model with application to metal deposit-related minerals mapping , 2017 .

[121]  David P. Roy,et al.  A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring , 2017, Remote. Sens..

[122]  Alan C. Bovik,et al.  Surface Water Mapping by Deep Learning , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[123]  Jian Sun,et al.  Preliminary Assessment of Wind and Wave Retrieval from Chinese Gaofen-3 SAR Imagery , 2017, Sensors.

[124]  Helmi Zulhaidi Mohd Shafri,et al.  Optimized Neural Architecture for Automatic Landslide Detection from High‐Resolution Airborne Laser Scanning Data , 2017 .

[125]  Fang Chen,et al.  A new technique for landslide mapping from a large-scale remote sensed image: A case study of Central Nepal , 2017, Comput. Geosci..

[126]  David J. Harding,et al.  The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation , 2017 .

[127]  Garrison W. Cottrell,et al.  Understanding Convolution for Semantic Segmentation , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[128]  Michael Thiel,et al.  High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models , 2017, PloS one.

[129]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[130]  Kevin Tansey,et al.  Evaluating the Use of an Object-Based Approach to Lithological Mapping in Vegetated Terrain , 2016, Remote. Sens..

[131]  Davide Castelvecchi,et al.  Can we open the black box of AI? , 2016, Nature.

[132]  Nicola Casagli,et al.  Landslide mapping and monitoring by using radar and optical remote sensing: examples from the EC-FP7 project SAFER , 2016 .

[133]  Luis Guanter,et al.  Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images , 2016, Remote. Sens..

[134]  Tiit Kutser,et al.  First Experiences in Mapping Lake Water Quality Parameters with Sentinel-2 MSI Imagery , 2016, Remote. Sens..

[135]  T. Kondo,et al.  GEOLOGICAL MAPPING BY COMBINING SPECTRAL UNMIXING AND CLUSTER ANALYSIS FOR HYPERSPECTRAL DATA , 2016 .

[136]  Paolo Gamba,et al.  Multi-feature combined cloud and cloud shadow detection in GF-1 WFV imagery , 2016, ArXiv.

[137]  Lichao Mou,et al.  Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection , 2016, Remote. Sens..

[138]  Carlos Roberto de Souza Filho,et al.  A review on spectral processing methods for geological remote sensing , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[139]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[140]  Jin Zhang,et al.  An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping , 2016 .

[141]  Paulo Pereira,et al.  Soil mapping, classification, and pedologic modeling: History and future directions , 2016 .

[142]  David J. Selkowitz,et al.  An Automated Approach for Mapping Persistent Ice and Snow Cover over High Latitude Regions , 2015, Remote. Sens..

[143]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[144]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[145]  B. Robson,et al.  Automated classification of debris-covered glaciers combining optical, SAR and topographic data in an object-based environment , 2015 .

[146]  Liangpei Zhang,et al.  Automatic Labelling and Selection of Training Samples for High-Resolution Remote Sensing Image Classification over Urban Areas , 2015, Remote. Sens..

[147]  Nirmal Kumar,et al.  High resolution remote sensing, GPS and GIS in soil resource mapping and characterization- A Review , 2015 .

[148]  Gang Yang,et al.  Missing Information Reconstruction of Remote Sensing Data: A Technical Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[149]  Chao Wang,et al.  High-Resolution Mapping of Urban Surface Water Using ZY-3 Multi-Spectral Imagery , 2015, Remote. Sens..

[150]  M. Farajzadeh,et al.  Use of multitemporal satellite images to find some evidence for glacier changes in the Haft-Khan glacier, Iran , 2015, Arabian Journal of Geosciences.

[151]  Yi-Leh Wu,et al.  Adaptive density-based spatial clustering of applications with noise (DBSCAN) according to data , 2015, 2015 International Conference on Machine Learning and Cybernetics (ICMLC).

[152]  Rob Fergus,et al.  Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.

[153]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[154]  Liangpei Zhang,et al.  Combining Pixel- and Object-Based Machine Learning for Identification of Water-Body Types From Urban High-Resolution Remote-Sensing Imagery , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[155]  M. Faisal,et al.  Mapping of hydrothermal alteration zones associated with potential sulfide mineralization using the spectral linear unmixing technique and WorldView II images at Wadi Rofaiyed, South Sinai, Egypt , 2015, Arabian Journal of Geosciences.

[156]  Gustau Camps-Valls,et al.  Measuring the Spatial and Spectral Performance of WorldView-3 , 2015 .

[157]  Thomas C. Edwards,et al.  Machine learning for predicting soil classes in three semi-arid landscapes , 2015 .

[158]  Larry Leigh,et al.  The Ground-Based Absolute Radiometric Calibration of Landsat 8 OLI , 2015, Remote. Sens..

[159]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[160]  Kenton Lee,et al.  The Spectral Response of the Landsat-8 Operational Land Imager , 2014, Remote. Sens..

[161]  M. Dirk Robinson,et al.  SkySat-1: very high-resolution imagery from a small satellite , 2014, SPIE Remote Sensing.

[162]  Piotr Duda,et al.  The CART decision tree for mining data streams , 2014, Inf. Sci..

[163]  Sergey V. Samsonov,et al.  Mapping and monitoring geological hazards using optical, LiDAR, and synthetic aperture RADAR image data , 2014, Natural Hazards.

[164]  Martha C. Anderson,et al.  Landsat-8: Science and Product Vision for Terrestrial Global Change Research , 2014 .

[165]  Hongsheng Zhang,et al.  Improving the impervious surface estimation with combined use of optical and SAR remote sensing images , 2014 .

[166]  Matthew J. Cracknell,et al.  Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information , 2014, Comput. Geosci..

[167]  Vinod Ramnath,et al.  CZMIL (coastal zone mapping and imaging lidar): from first flights to first mission through system validation , 2013, Defense, Security, and Sensing.

[168]  Renata Ribeiro do Valle Gonçalves,et al.  Coffee Crop's Biomass and Carbon Stock Estimation With Usage of High Resolution Satellites Images , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[169]  Fabio Remondino,et al.  EVALUATION OF PLEIADES-1A TRIPLET ON TRENTO TESTFIELD , 2013 .

[170]  Manuel A. Aguilar,et al.  GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments , 2013 .

[171]  Karsten Jacobsen,et al.  Geometric accuracy and information content of WorldView-1 images , 2013 .

[172]  Libor Preucil,et al.  Coordination and navigation of heterogeneous UAVs-UGVs teams localized by a hawk-eye approach , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[173]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[174]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[175]  A. Brenning,et al.  Detecting rock glacier flow structures using Gabor filters and IKONOS imagery , 2012 .

[176]  Onisimo Mutanga,et al.  High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[177]  John L. Dwyer,et al.  Landsat: building a strong future , 2012 .

[178]  S. Horion,et al.  Satellite remote sensing for soil mapping in Africa , 2012 .

[179]  Malcolm Davidson,et al.  GMES Sentinel-1 mission , 2012 .

[180]  Ishuwa C. Sikaneta,et al.  Optimum SAR/GMTI Processing and Its Application to the Radar Satellite RADARSAT-2 for Traffic Monitoring , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[181]  Ding Feng,et al.  A New Method for Fast Information Extraction of Water Bodies Using Remotely Sensed Data , 2012 .

[182]  Tsehaie Woldai,et al.  Multi- and hyperspectral geologic remote sensing: A review , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[183]  Jie Lun Chiang,et al.  Reservoir Drought Prediction Using Support Vector Machines , 2011 .

[184]  Constantine Caramanis,et al.  Robust PCA via Outlier Pursuit , 2010, IEEE Transactions on Information Theory.

[185]  Grady Tuell,et al.  Overview of the coastal zone mapping and imaging lidar (CZMIL): a new multisensor airborne mapping system for the U.S. Army Corps of Engineers , 2010, Defense + Commercial Sensing.

[186]  Helen Amanda Fricker,et al.  The ICESat-2 Laser Altimetry Mission , 2010, Proceedings of the IEEE.

[187]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[188]  Brian Menounos,et al.  Contribution of Alaskan glaciers to sea-level rise derived from satellite imagery , 2010 .

[189]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[190]  Leif E. Peterson K-nearest neighbor , 2009, Scholarpedia.

[191]  A. Brenning Benchmarking classifiers to optimally integrate terrain analysis and multispectral remote sensing in automatic rock glacier detection , 2009 .

[192]  Anil K. Jain Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[193]  Michael J. Brett,et al.  Glancing angle deposition: Fabrication, properties, and applications of micro- and nanostructured thin films , 2007 .

[194]  T. Kusky,et al.  ASTER spectral ratioing for lithological mapping in the Arabian–Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt , 2007 .

[195]  Saro Lee Application and verification of fuzzy algebraic operators to landslide susceptibility mapping , 2007 .

[196]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[197]  Nazzareno Pierdicca,et al.  Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies , 2006 .

[198]  Hanqiu Xu Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[199]  L. Rowan,et al.  Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) , 2005 .

[200]  J. Beck,et al.  An introduction to the RADARSAT-2 mission , 2004 .

[201]  R. Bryant,et al.  Data continuity of Earth Observing 1 (EO-1) Advanced Land I satellite imager (ALI) and Landsat TM and ETM+ , 2003, IEEE Trans. Geosci. Remote. Sens..

[202]  Vittorio E. Brando,et al.  Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality , 2003, IEEE Trans. Geosci. Remote. Sens..

[203]  Gaurav S. Sukhatme,et al.  Visually guided landing of an unmanned aerial vehicle , 2003, IEEE Trans. Robotics Autom..

[204]  F. Wellmer,et al.  Sustainable development and the exploitation of mineral and energy resources: a review , 2002 .

[205]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[206]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[207]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[208]  T. D. Clark,et al.  Electric potential probes - new directions in the remote sensing of the human body , 2002 .

[209]  Darrel L. Williams,et al.  The Landsat 7 mission: terrestrial research and applications for the 21st century , 2001 .

[210]  F. Lehmann,et al.  HyMap hyperspectral remote sensing to detect hydrocarbons , 2001 .

[211]  C. Vörösmarty,et al.  Global water resources: vulnerability from climate change and population growth. , 2000, Science.

[212]  S. N. Lane,et al.  Application of Digital Photogrammetry to Complex Topography for Geomorphological Research , 2000 .

[213]  John M. Reynolds,et al.  An overview of glacial hazards in the Himalayas , 2000 .

[214]  J. Mustard,et al.  Ronda peridotite massif: Methodology for its geological mapping and lithological discrimination from airborne hyperspectral data , 2000 .

[215]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[216]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[217]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[218]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[219]  S. K. McFeeters The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[220]  Roger N. Clark,et al.  Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice and snow, and other materials: The USGS tricorder algorithm , 1995 .

[221]  A. Goetz,et al.  Geologic remote sensing. , 1981, Science.

[222]  Hai-bo Li,et al.  A fusion method using terrestrial laser scanning and unmanned aerial vehicle photogrammetry for landslide deformation monitoring under complex terrain conditions , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[223]  Wei Wu,et al.  Identification of Soil Texture Classes Under Vegetation Cover Based on Sentinel-2 Data With SVM and SHAP Techniques , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[224]  Yusen Dong,et al.  Geological Remote Sensing Interpretation Using Deep Learning Feature and an Adaptive Multisource Data Fusion Network , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[225]  Mi Wang,et al.  Robust Correction of Relative Geometric Errors Among GaoFen-7 Regional Stereo Images Based on Posteriori Compensation , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[226]  Xiaoshuang Ma,et al.  Water Body Automated Extraction in Polarization SAR Images With Dense-Coordinate-Feature-Concatenate Network , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[227]  E. Cambria,et al.  Deep Learning--based Text Classification , 2020, ACM Comput. Surv..

[228]  Juanle Wang,et al.  Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[229]  Shen-En Qian,et al.  Hyperspectral Satellites, Evolution, and Development History , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[230]  Penghai Wu,et al.  A Deep Learning Method of Water Body Extraction From High Resolution Remote Sensing Images With Multisensors , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[231]  Khan Muhammad,et al.  Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data , 2021, ISPRS Int. J. Geo Inf..

[232]  Jun Dai,et al.  Design and Data Processing of China's First Spaceborne Laser Altimeter System for Earth Observation: GaoFen-7 , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[233]  Dastan Hussen Maulud,et al.  A Review on Linear Regression Comprehensive in Machine Learning , 2020 .

[234]  Hai Huang,et al.  BAS$^{4}$Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[235]  Vijayan K. Asari,et al.  GlacierNet: A Deep-Learning Approach for Debris-Covered Glacier Mapping , 2020, IEEE Access.

[236]  Philip S. Yu,et al.  A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[237]  Benoit Rivard,et al.  Geological remote sensing , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[238]  D. Djenouri,et al.  Machine Learning for Smart Building Applications: Review and Taxonomy , 2018 .

[239]  Ying Liu,et al.  Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning , 2016 .

[240]  Fionn Murtagh,et al.  Algorithms for hierarchical clustering: an overview , 2012, WIREs Data Mining Knowl. Discov..

[241]  Zili Zhang,et al.  Missing Value Estimation for Mixed-Attribute Data Sets , 2011, IEEE Transactions on Knowledge and Data Engineering.

[242]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[243]  Yan Pei,et al.  A Study on Information Extraction of Water System in Semi-arid Regions with the Enhanced Water Index(EWI) and GIS Based Noise Remove Techniques , 2007 .

[244]  P. Frazier,et al.  Water body detection and delineation with Landsat TM data. , 2000 .

[245]  P. R. Meneses,et al.  Spectral Correlation Mapper ( SCM ) : An Improvement on the Spectral Angle Mapper ( SAM ) , 2000 .