A patch-based convolutional neural network for remote sensing image classification

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Object-based multi-modal convolution neural networks for building extraction using panchromatic and multispectral imagery

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V-NET-VGG16: Hybrid deep learning architecture for optimal segmentation and classification of multi-differentiated liver tumors

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BRCN-ERN: A Bidirectional Reconstruction Coding Network and Enhanced Residual Network for Hyperspectral Change Detection

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An Adaptive Capsule Network for Hyperspectral Remote Sensing Classification

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An integrated approach of deep learning convolutional neural network and google earth engine for salt storm monitoring and mapping

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A Restoration Scheme for Spatial and Spectral Resolution of the Panchromatic Image Using the Convolutional Neural Network

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Mapping invasive noxious weed species in the alpine grassland ecosystems using very high spatial resolution UAV hyperspectral imagery and a novel deep learning model

Xing, F., An, R., Guo, X., & Shen, X. (2024) · GIScience & Remote Sensing · doi:10.1080/15481603.2024.2327146 · Cited by 21

Integration of Object-Based Image Analysis and Convolutional Neural Network for the Classification of High-Resolution Satellite Image: A Comparative Assessment

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DSViT: Dynamically Scalable Vision Transformer for Remote Sensing Image Segmentation and Classification

Wang, F., Ji, J., & Wang, Y. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3285259 · Cited by 20

Unsupervised domain adaptive building semantic segmentation network by edge-enhanced contrastive learning

Yang, M., Yang, R., Tao, S., Zhang, X., & Wang, M. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106581 · Cited by 20

Mapping Forest Degradation and Contributing Factors in a Tropical Dry Forest

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Mechanisms of vegetation restoration at landslides caused by a catastrophic earthquake in Central Taiwan

Lin, W., Huang, P.-H., & Chou, T. (2023) · Ecological Engineering · doi:10.1016/j.ecoleng.2023.106929 · Cited by 18

Multiscale Feature Extraction by Using Convolutional Neural Network: Extraction of Objects from Multiresolution Images of Urban Areas

Fan, C. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13010005 · Cited by 18

Trainable spectral difference learning with spatial starting for hyperspectral image denoising

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Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification

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Attribute-guided feature fusion network with knowledge-inspired attention mechanism for multi-source remote sensing classification

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Development of a deep convolutional neural network model for detection and delineation of coal mining regions

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Multi-Classification of Satellite Imagery Using Fully Convolutional Neural Network

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Estimating environmental vulnerability in the Cerrado with machine learning and Twitter data

Luo, D., Caldas, M. M., & Goodin, D. G. (2021) · Journal of Environmental Management · doi:10.1016/j.jenvman.2021.112502 · Cited by 16

Hopfield type of Artificial Neural Network via Election Algorithm as Heuristic Search method for Random Boolean kSatisfiability

Abubakar, H., & Danrimi, M. L. (2021) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/100163 · Cited by 16

End-to-End Hyperspectral Image Change Detection Based on Band Selection

Yao, Q., Zhou, Y., Tang, C., Xiang, W., & Zheng, G. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3382638 · Cited by 15

Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network

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A Novel Boundary Loss Function in Deep Convolutional Networks to Improve the Buildings Extraction From High-Resolution Remote Sensing Images

Hosseinpour, H., Samadzadegan, F., & Javan, F. D. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3178470 · Cited by 15

Design and Optimization of CNN Architecture to Identify the Types of Damage Imagery

Fan, C., & Chung, Y.-J. (2022) · Mathematics · doi:10.3390/math10193483 · Cited by 16

A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas

Salvo, C., & Vitale, A. (2023) · Remote Sensing · doi:10.3390/rs15174288 · Cited by 14

Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks

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High-Resolution Imagery Classification Based on Different Levels of Information

Li, E., Samat, A., Liu, W., Lin, C., & Bai, X. (2019) · Remote Sensing · doi:10.3390/rs11242916 · Cited by 14

Fuzzy neighbourhood neural network for high-resolution remote sensing image segmentation

Qu, T., Xu, J., Chong, Q., Liu, Z., Yan, W., Wang, X., Song, Y., & Ni, M. (2023) · European Journal of Remote Sensing · doi:10.1080/22797254.2023.2174706 · Cited by 14

APPLICATION OF DEEP LEARNING OF MULTI-TEMPORAL SENTINEL-1 IMAGES FOR THE CLASSIFICATION OF COASTAL VEGETATION ZONE OF THE DANUBE DELTA

Niculescu, S., Ienco, D., & Hanganu, J. (2018) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-3-1311-2018 · Cited by 14

Effectiveness of autoencoder for lake area extraction from high-resolution RGB imagery: an experimental study

Tercan, E., & Atasever, Ü. H. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-12893-y · Cited by 14

Integration of convolutional neural networks for flood risk mapping in Tuscany, Italy

Kotaridis, I., & Lazaridou, M. A. (2022) · Natural Hazards · doi:10.1007/s11069-022-05525-2 · Cited by 13

Effects of Class Purity of Training Patch on Classification Performance of Crop Classification with Convolutional Neural Network

Park, S., & Park, N.-W. (2020) · Applied Sciences · doi:10.3390/app10113773 · Cited by 13

Sentinel-2 Remote Sensed Image Classification with Patchwise Trained ConvNets for Grassland Habitat Discrimination

Fazzini, P., Proia, G. D. F., Adamo, M., Blonda, P. N., Petracchini, F., Forte, L., & Tarantino, C. (2021) · Remote Sensing · doi:10.3390/rs13122276 · Cited by 14

LOVD: Land Vehicle Detection in Complex Scenes of Optical Remote Sensing Image

Yan, P., Liu, X., Wang, F., Yue, C., & Wang, X. Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3133109 · Cited by 13

A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery

Zhang, C., Han, Y., Feng, L., Gao, S., Song, D., Zhao, H. Y., Fan, K., & Zhang, Y. (2019) · Remote Sensing · doi:10.3390/rs11060619 · Cited by 13

Using convolutional neural networks to identify illegal roofs from unmanned aerial vehicle images

Fan, C. (2023) · Architectural Engineering and Design Management · doi:10.1080/17452007.2023.2244949 · Cited by 12

Surrounding rock classification from onsite images with deep transfer learning based on EfficientNet

Zhuang, X., Fan, W., Guo, H., Chen, X., & Wang, Q. (2024) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-024-1134-7 · Cited by 12

Dynamic Analysis of Spartina alterniflora in Yellow River Delta Based on U-Net Model and Zhuhai-1 Satellite

Li, H., Cui, G., Liu, H., Wang, Q., Zhao, S., Huang, X., Zhang, R., Jia, M., Mao, D., Yu, H., et al. (2025) · Remote Sensing · doi:10.3390/rs17020226 · Cited by 12

Multispectral Data Analysis for Semantic Assessment—A SNAP Framework for Sentinel-2 Use Case Scenarios

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Classification of high resolution satellite images using improved U-Net

Wang, Y., Zhang, D., & Dai, G. (2020) · International Journal of Applied Mathematics and Computer Science · doi:10.34768/amcs-2020-0030 · Cited by 12

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

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Deep Learning for Typhoon Intensity Classification Using Satellite Cloud Images

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Improving Urban Land Cover/Use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy

Luo, X., Tong, X., Hu, Z., & Wu, G. (2020) · Remote Sensing · doi:10.3390/rs12142292 · Cited by 11

High-Level Design and Rapid Implementation of a Clinical and Non-clinical Blockchain-Based Data Sharing Platform for COVID-19 Containment

Saleh, S. N., & Shayor, F. (2020) · Frontiers in Blockchain · doi:10.3389/fbloc.2020.553257 · Cited by 11

A Comparative Analysis of Different Land-use and Land-cover Classifiers using Hyperspectral Data

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AFD filter and E2N2 classifier for improving visualization of crop image and crop classification in remote sensing image

Pandey, S., Miri, R., Sinha, G. R., & Raja, R. (2022) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.2000062 · Cited by 10

Remote Sensing Monitoring of Grasslands Based on Adaptive Feature Fusion with Multi-Source Data

Wang, W., Ma, Q., Huang, J., Feng, Q., Zhao, Y., Guo, H., Chen, B., Li, C., & Zhang, Y. (2022) · Remote Sensing · doi:10.3390/rs14030750 · Cited by 10

Assessment of milling condition by image processing of the produced surfaces

Carbone, N., Bernini, L., Albertelli, P., & Monno, M. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-10516-5 · Cited by 10

AgF²Net: Attention-Guided Feature Fusion Network for Multitemporal Hyperspectral Image Change Detection

Wang, X., Ni, W., Feng, Y., & Song, L. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3302469 · Cited by 10

SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification

Syrris, V., Pešek, O., & Soille, P. (2020) · Remote Sensing · doi:10.3390/rs12203358 · Cited by 10

InfraNet: Accurate forehead temperature measurement framework for people in the wild with monocular thermal infrared camera

Zhou, X., Lei, D., Long, C., Nie, J., & Liu, H. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.07.038 · Cited by 9

Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks

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Crop Type Mapping Based on Polarization Information of Time Series Sentinel-1 Images Using Patch-Based Neural Network

Liu, Y., Pu, X., & Shen, Z. (2023) · Remote Sensing · doi:10.3390/rs15133384 · Cited by 9

Mining the features of spatial adjacency relationships to improve the classification of high resolution remote sensing images based on complex network

Wang, C., Shao, F., Zhang, Z., Sui, Y., & Li, S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107089 · Cited by 8

Main Path Analysis to Filter Unbiased Literature

Umair, M., Majeed, F., Shoaib, M., Saleem, M. Q., Adrees, M. S., Karrar, A. E., Khurram, S., Shafiq, M., & Choi, J. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.018952 · Cited by 8

Synthetic polarization-sensitive optical coherence tomography using contrastive unpaired translation

Le, T. D., Lee, Y.-J., Park, E., Kim, M., Eom, T. J., & Lee, C.-H. (2024) · Scientific Reports · doi:10.1038/s41598-024-82839-0 · Cited by 7

Fusion of SAR and optical images using pixel-based CNN

Bandi, S. R., Anbarasan, M., & Sheela, D. (2022) · Neural Network World · doi:10.14311/nnw.2022.32.012 · Cited by 7

From Pixels to Deposits: Porphyry Mineralization With Multispectral Convolutional Neural Networks

Zidan, U., Desouky, H. A. E., Gaber, M. M., & Abdelsamea, M. M. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3321714 · Cited by 7

Land cover mapping of mixed classes using 2D CNN with multi-frequency SAR data

Kukunuri, A. N. J., Phartiyal, G. S., & Singh, D. (2024) · Advances in Space Research · doi:10.1016/j.asr.2024.03.066 · Cited by 6

Efficient 3D convolutional neural networks for Sentinel-2 land cover classification with limited ground truth data

Carneiro, G. A., Svoboda, J., Cunha, A. M. T. D. S., Sousa, J. J., & Štych, P. (2025) · European Journal of Remote Sensing · doi:10.1080/22797254.2025.2512531 · Cited by 8

Progress in applications of self-supervised learning to computer vision in agriculture: A systematic review

Carneiro, G. A., Aubry, T., Cunha, A. M. T. D. S., Radeva, P. I., & Sousa, J. J. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.111134 · Cited by 8

Deep learning models to map an agricultural expansion area with MODIS and Sentinel-2 time series images

Luo, D., Caldas, M. M., & Yang, H. (2022) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.16.046508 · Cited by 6

Automated actual evapotranspiration estimation: Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms

Bafti, A. G., Ahmadi, A., Abbasi, A., Kamangir, H., Jamali, S., & Hashemi, H. (2023) · Atmospheric Research · doi:10.1016/j.atmosres.2023.107107 · Cited by 6

Open-Pit Mining Area Extraction Using Multispectral Remote Sensing Images: A Deep Learning Extraction Method Based on Transformer

Qiao, Q., Li, Y., & Lv, H. (2024) · Applied Sciences · doi:10.3390/app14146384 · Cited by 8

Comparative evaluation of Pixel-Based and Object-Based classification approaches for Land Use/Land Cover mapping using deep learning on satellite data

Kumar, A., Ranjan, A. K., Paygude, P., & Daimary, R. (2025) · Applied Geomatics · doi:10.1007/s12518-025-00654-2 · Cited by 6

BTS: a binary tree sampling strategy for object identification based on deep learning

Lv, X., Shao, Z., Huang, X., Zhou, W., Ming, D., Wang, J., & Tong, C. (2021) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2021.1980883 · Cited by 6

Study on Remote Sensing Image Classification of Oasis Area Based on ENVI Deep Learning

Ma, H., Zhao, W., Li, F., Yan, H., & Liu, Y. (2023) · Polish Journal of Environmental Studies · doi:10.15244/pjoes/160190 · Cited by 6

EVALUATION OF EFFECTIVENESS OF PATCH BASED IMAGE CLASSIFICATION TECHNIQUE USING HIGH RESOLUTION WORLDVIEW-2 IMAGE

Öztürk, M. Y., & Çölkesen, İ. (2021) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlvi-4-w5-2021-417-2021 · Cited by 6

A Multilane Tracking Algorithm Using IPDA with Intensity Feature

Akbari, B., Thiyagalingam, J., Lee, R., & Thia, K. (2021) · Sensors · doi:10.3390/s21020461 · Cited by 6

SCFN: A Deep Network for Functional Urban Impervious Surface Mapping Using C -Band and L -Band Polarimetric SAR Data

Ling, J., Zhang, H., Liu, R., & Lin, Y. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3427839 · Cited by 5

Mitigation of numerical dispersion in seismic data in spectral domain with neural networks

Gadylshin, K., Gondyul, E., Lisitsa, V. V., Gadylshina, K., & Vishnevsky, D. (2024) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2024.109028 · Cited by 5

Data Mining Problems Classification and Techniques

Rahman, N. (2018) · International Journal of Big Data and Analytics in Healthcare · doi:10.4018/ijbdah.2018010104 · Cited by 5

Evaluation of spectral similarity indices in unsupervised change detection approaches

Ramos, J. F., Renza, D., & Ballesteros, D. M. (2018) · DYNA · doi:10.15446/dyna.v85n204.68355 · Cited by 5

Restoration Guarantee of Image Inpainting via Low Rank Patch Matrix Completion

Jian-feng Cai, A., Choi, J. K., Li, J., & Yin, G. (2024) · SIAM Journal on Imaging Sciences · doi:10.1137/23m1614456 · Cited by 4

Mapping tropical dry Miombo woodlands into functional forest classes using Sentinel-1 and Sentinel-2 imagery and machine learning

Kanja, K., Zhang, C., Lippe, M., & Atkinson, P. M. (2025) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2025.101615 · Cited by 4

Image Classification using Advanced CNN Based on Tensorflow

Garchar, V., & Chudhary, S. (2019) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2064121 · Cited by 4

DAT-CNN: Dual Attention Temporal CNN for Time-Resolving Sentinel-3 Vegetation Indices

Ibañez, D., Fernández-Beltrán, R., Pla, F., & Yokoya, N. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3161190 · Cited by 4

Exploration of convolutional neural network architectures for large region map automation

Tsenov, R.-M., Henry, C. J., Storie, J. L., Storie, C. D., Murray, B. A., & Sokolov, M. (2023) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.17.018505 · Cited by 4

Assessing CNN and Semantic Segmentation Models for Coarse Resolution Satellite Image Classification in Subcontinental Scale Land Cover Mapping

Adugna, T., Xu, W., Fan, J., Jia, H., & Luo, X. Y. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3469728 · Cited by 4

Deep Learning Spatial-Spectral Classification of Remote Sensing Images by Applying Morphology-Based Differential Extinction Profile (DEP)

Kakhani, N., Mokhtarzade, M., & Zoej, M. J. V. (2021) · Electronics · doi:10.3390/electronics10232893 · Cited by 4

SAU-Net: A Deep Learning Approach for Glacier Mapping Based on Multisource Remote Sensing Data

Xiang, Y., Zhao, L., Li, J., Gao, F., Bian, S., Hou, M., Luo, X., & Guo, C. (2025) · IEEE Access · doi:10.1109/access.2025.3542834 · Cited by 3

Identifying Illegal Buildings Using UAV Images and Faster R-CNN Based on Deep Learning

Fan, C. (2023) · Advances in intelligent systems and computing · doi:10.1007/978-981-99-3608-3_35 · Cited by 3

A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification

Supritha, N., & S, N. M. M. (2023) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.110301 · Cited by 3

Patch-Based Capsule Network for Complex Images

Ayidzoe, M. A., Yu, Y., Mensah, P. K., Cai, J., Bawah, F. U., & Nyarko‐Boateng, O. (2021) · IEEE International Conference on Adaptive Science & Technology · doi:10.1109/icast52759.2021.9681959 · Cited by 3

Deep Learning Based Image Fusion Applied on Landsat-8 Archival Data Using UVCGAN

Kambham, S. P., & Bharathi, D. (2023) · Innovations in Power and Advanced Computing Technologies (i-PACT) · doi:10.1109/i-pact58649.2023.10434448 · Cited by 3

Extraction of buildings from remote sensing imagery based on multi-scale SLIC-GMRF and FCNSVM

Ran, J., Zhaoning, G., Wending, Z. H. U., Guan, H., Wenji, Z., & Tao, Z. (2020) · National Remote Sensing Bulletin · doi:10.11834/jrs.20208221 · Cited by 3

How to accurately extract large-scale urban land? Establishment of an improved fully convolutional neural network model

Yin, B., Guan, D., Zhang, Y., He, X. Y., Cheng, L., Cao, J., & Su, X. (2022) · Frontiers of Earth Science · doi:10.1007/s11707-022-0985-2 · Cited by 3

Convolutional Neural Network with Expert Knowledge for Hyperspectral Remote Sensing Imagery Classification

Wu, C., Wang, M., Gao, L., Song, W., Tian, T., & Choo, K. R. (2019) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2019.08.006 · Cited by 3

Mapping of urban landuse and landcover with multiple sensors : Joining close and remote sensing with deep learning

Srivastava, S. (2020) · doi:10.18174/509667 · Cited by 3

Deep Learning for Urban and Landscape Mapping from Remotely Sensed Imagery

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