DeepLab-Based Spatial Feature Extraction for Hyperspectral Image Classification
Abstract
Recently, deep learning has been used for hyperspectral image classification (HSIC) due to its powerful feature learning and classification ability. In this letter, a novel deep learning-based framework based on DeepLab is proposed for HSIC. Inspired by the excellent performance of DeepLab in semantic segmentation, the proposed framework applies DeepLab to excavate spatial features of the hyperspectral image (HSI) pixel to pixel. It breaks through the limitation of patch-wise feature learning in the most of existing deep learning methods used in HSIC. More importantly, it can extract features at multiple scales and effectively avoid the reduction of spatial resolution. Furthermore, to improve the HSIC performance, the spatial features extracted by DeepLab and the spectral features are fused by a weighted fusion method, then the fused features are input into support vector machine for final classification. Experimental results on two public HSI data sets demonstrate that the proposed framework outperformed the traditional methods and the existing deep learning-based methods, especially for small-scale classes.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Models like U-Net and DeepLab can be used for pixel-wise classification, which is useful in highly dense and occluded environments when individuals need to be segmented precisely and distinguish people from backgrounds at a pixel level.
“Models like U-Net [58] and DeepLab [59] can be used for pixel-wise classification, which is useful in highly dense and occluded environments when individuals need to be segmented precisely and distinguish people from backgrounds at a pixel level.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [59]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
Self-citation(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · see citing works
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971 · see citing works
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213 · see citing works
Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2584107 · Cited by 3,005 · see citing works
Deep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679 · see citing works
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
Li, Y., Zhang, H., & Shen, Q. (2017) · Remote Sensing · doi:10.3390/rs9010067 · Cited by 1,340 · see citing works
Advances in Spectral-Spatial Classification of Hyperspectral Images
Fauvel, M., Tarabalka, Y., Benediktsson, J. A., Chanussot, J., & Tilton, J. C. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2197589 · Cited by 1,288 · see citing works
Deep supervised learning for hyperspectral data classification through convolutional neural networks
Makantasis, K., Karantzalos, K. G., Doulamis, A. D., & Doulamis, N. D. (2015) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2015.7326945 · Cited by 1,045 · see citing works
Remote Sensing Digital Image Analysis
Richards, J. A. (2012) · Springer eBooks · doi:10.1007/978-3-642-30062-2 · Cited by 657 · see citing works
Boardman, J. W., & Kruse, F. A. (1994) · Cited by 417 · see citing works
Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation
Fang, L., Li, S., Kang, X., & Benediktsson, J. A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2318058 · Cited by 321 · see citing works
Supervised Classification of Remotely Sensed Imagery Using a Modified $k$-NN Technique
Samaniego, L., Bàrdossy, A., & Schulz, K. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.916629 · Cited by 182 · see citing works
Classification of Hyperspectral Imagery Using a New Fully Convolutional Neural Network
Li, J., Zhao, X. B., Li, Y., Du, Q., Xi, B., & Hu, J. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2786272 · Cited by 180 · see citing works
Hierarchical maximum-likelihood classification for improved accuracies
Ediriwickrema, J., & Khorram, S. (1997) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.602523 · Cited by 65 · see citing works
SVM-based hyperspectral image classification using intrinsic dimension
Hasanlou, M., Samadzadegan, F., & Homayouni, S. (2013) · Arabian Journal of Geosciences · doi:10.1007/s12517-013-1141-9 · Cited by 27 · see citing works
Flagged first, then by citations
3 of the 22 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A review of deep learning used in the hyperspectral image analysis for agriculture
Wang, C., Liu, B., Liu, L., Zhu, Y., Hou, J., Liu, P., & Li, X. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10018-y · Cited by 365 · see citing works
Imani, M., & Ghassemian, H. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.007 · Cited by 321 · see citing works
A Supervised Segmentation Network for Hyperspectral Image Classification
Sun, H., Zheng, X., & Lu, X. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3055613 · Cited by 206 · see citing works
Big Data and Machine Learning With Hyperspectral Information in Agriculture
Ang, L.-M., & Seng, J. K. P. (2021) · IEEE Access · doi:10.1109/access.2021.3051196 · Cited by 202 · see citing works
Zhu, Q., Deng, W., Zheng, Z., Zhong, Y., Guan, Q., Lin, W., Zhang, L., & Li, D. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3070577 · Cited by 195 · see citing works
Tang, X., Meng, F., Zhang, X., Cheung, Y., Ma, J., Liu, F., & Jiao, L. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3005431 · Cited by 150 · see citing works
Identification of Melanoma From Hyperspectral Pathology Image Using 3D Convolutional Networks
Wang, Q., Li, S., Wang, Y., Zhou, M., Hu, M., Chen, J., Wen, Y., & Li, Q. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3024923 · Cited by 143 · see citing works
Niu, B., Feng, Q., Chen, B., Ou, C., Liu, Y., & Yang, J. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107297 · Cited by 114 · see citing works
Ahmad, M., Distefano, S., Khan, A., Mazzara, M., Li, C., Li, H., Aryal, J., Ding, Y., Vivone, G., & Hong, D. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130428 · Cited by 107 · see citing works
Hyperspectral Band Selection Using Attention-Based Convolutional Neural Networks
Lorenzo, P. R., Tulczyjew, Ł., Marcinkiewicz, M., & Nalepa, J. (2020) · IEEE Access · doi:10.1109/access.2020.2977454 · Cited by 104 · see citing works
DSSNet: A Simple Dilated Semantic Segmentation Network for Hyperspectral Imagery Classification
Pan, B., Xu, X., Shi, Z., Zhang, N., Luo, H., & Lan, X. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2960528 · Cited by 96 · see citing works
Fast Task-Specific Region Merging for SAR Image Segmentation
Ma, F., Zhang, F., Xiang, D., Yin, Q., & Zhou, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3141125 · Cited by 95 · see citing works
A Review of Optical and SAR Image Deep Feature Fusion in Semantic Segmentation
Liu, C., Sun, Y., Xu, Y., Sun, Z., Zhang, X., Lei, L., & Kuang, G. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3424831 · Cited by 95 · see citing works
DPSNet: Multitask Learning Using Geometry Reasoning for Scene Depth and Semantics
Zhang, J., Su, Q., Tang, B., Wang, C., & Li, Y. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3107362 · Cited by 84 · see citing works
Sun, G., Zhang, X., Jia, X., Ren, J., Zhang, A., Yao, Y., & Zhao, H. (2020) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2020.102157 · Cited by 77 · see citing works
Hyperspectral Image Classification Based on Multibranch Attention Transformer Networks
Bai, J., Wen, Z., Xiao, Z. Q., Ye, F., Zhu, Y., Alazab, M., & Jiao, L. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3196661 · Cited by 65 · see citing works
Towards resource-frugal deep convolutional neural networks for hyperspectral image segmentation
Nalepa, J., Antoniak, M., Myller, M., Lorenzo, P. R., & Marcinkiewicz, M. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.102994 · Cited by 62 · see citing works
Ren, Y., Zhang, X., Ma, Y., Yang, Q., Wang, C., Liu, H., & Qi, Q. (2020) · Remote Sensing · doi:10.3390/rs12213547 · Cited by 55 · see citing works
Automatic segmentation of cardiac magnetic resonance images based on multi-input fusion network
Shi, J., Ye, Y., Zhu, D., Su, L., Huang, Y., & Huang, J. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106323 · Cited by 51 · see citing works
Ye, L., Wang, X., An, A., & Guan, H. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01415-x · Cited by 51 · see citing works
Class-Aware Domain Adaptation for Semantic Segmentation of Remote Sensing Images
Xu, Q., Yuan, X., & Ouyang, C. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3031926 · Cited by 45 · see citing works
Diao, Z., Guo, P., Zhang, B., Yan, J., He, Z., Zhao, S., Zhao, C., & Zhang, J. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108092 · Cited by 43 · see citing works
Semantic Segmentation for Remote Sensing Images Based on Adaptive Feature Selection Network
Xiang, S., Xie, G., & Wang, M. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3049125 · Cited by 43 · see citing works
SSNET: an improved deep hybrid network for hyperspectral image classification
Paul, A., Bhoumik, S., & Chaki, N. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05069-1 · Cited by 41 · see citing works
Mask-R-FCN: A Deep Fusion Network for Semantic Segmentation
Zhang, Y., & Chi, M. (2020) · IEEE Access · doi:10.1109/access.2020.3012701 · Cited by 35 · see citing works
Intelligent breast cancer diagnosis with two-stage using mammogram images
Yaqub, M., Feng, J., Aijaz, N., Ahmed, S., Mehmood, A., Jiang, H., & He, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-65926-0 · Cited by 32 · see citing works
Heryadi, Y., Irwansyah, E., Miranda, E., Soeparno, H., Herlawati, H., & Hashimoto, K. (2020) · IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS) · doi:10.1109/agers51788.2020.9452768 · Cited by 30 · see citing works
Liu, L., Shi, Z., Pan, B., Zhang, N., Luo, H., & Lan, X. (2020) · Remote Sensing · doi:10.3390/rs12020280 · Cited by 27 · see citing works
An infrared image based state evaluation method for cable incipient faults
Li, Z., Yang, H., Yang, F., Tan, T., Lu, X., & Tian, J. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108148 · Cited by 25 · see citing works
Jiang, P., Ma, Z., & Mei, G. (2026) · Natural hazards and earth system sciences · doi:10.5194/nhess-26-487-2026 · Cited by 30 · see citing works
A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data
Lu, H. Z., Liu, Q., Liu, X., & Zhang, Y. (2021) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.20211101.oa6 · Cited by 22 · see citing works
A Review on Research and Application of AI-Based Image Analysis in the Field of Computer Vision
Wu, P., He, X., Dai, W., Zhou, J., Shang, Y., Fan, Y., & Hu, T. (2025) · IEEE Access · doi:10.1109/access.2025.3565300 · Cited by 19 · see citing works
Hyperspectral Image Classification With Deep Metric Learning and Conditional Random Field
Liang, Y., Zhao, X. W., Guo, A. J. X., & Zhu, F. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2939356 · Cited by 19 · see citing works
Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24 · see citing works
AIoT-Based Meat Quality Monitoring Using Camera and Gas Sensor With Wireless Charging
Kim, D.-E., Mai, N.-D., & Chung, W. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3328915 · Cited by 18 · see citing works
MSLM-RF: A Spatial Feature Enhanced Random Forest for On-Board Hyperspectral Image Classification
Yuan, S., Sun, Y., He, W., Gu, Q., Xu, S., Mao, Z., & Tu, S. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3194075 · Cited by 16 · see citing works
An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+
Si, Y., Gong, D., Guo, Y., Zhu, X., Huang, Q., Evans, J. S., He, S., & Sun, Y. (2021) · Applied Sciences · doi:10.3390/app11125703 · Cited by 16 · see citing works
Wang, X., Zhang, Y., Lei, T., Wang, Y., Zhai, Y., & Nandi, A. K. (2022) · Remote Sensing · doi:10.3390/rs14194941 · Cited by 15 · see citing works
Su, H., Qiu, J., Tang, Z., Huang, Z., & Yan, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3350346 · Cited by 15 · see citing works
Deep learning algorithms for hyperspectral remote sensing classifications: an applied review
Pal, M. (2024) · International Journal of Remote Sensing · doi:10.1080/01431161.2023.2297178 · Cited by 14 · see citing works
Gui, B., Sam, L., Bhardwaj, A., Gómez, D. S., Peñaloza, F. G., Buchroithner, M. F., & Green, D. R. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.06.004 · Cited by 14 · see citing works
Guo, H., Sun, L., Yao, A., Chen, Z., Feng, H., Wu, S., & Siddique, K. H. M. (2023) · Land Degradation and Development · doi:10.1002/ldr.4612 · Cited by 13 · see citing works
Sussi, S., Husni, E. M., Yusuf, R., Harto, A. B., Suwardhi, D., & Siburian, A. (2024) · IEEE Access · doi:10.1109/access.2024.3397324 · Cited by 12 · see citing works
Leichter, A., Almeev, R., Wittich, D., Beckmann, P., Rottensteiner, F., Holtz, F., & Sester, M. (2022) · Frontiers in Earth Science · doi:10.3389/feart.2022.740638 · Cited by 12 · see citing works
A Multi Brain Tumor Region Segmentation Model Based on 3D U-Net
Li, Z., Wu, X., & Yang, X. (2023) · Applied Sciences · doi:10.3390/app13169282 · Cited by 9 · see citing works
An advanced skin lesion segmentation and classification framework using deep learning strategies
Deepa, J., & Madhavan, P. (2025) · Scientific Reports · doi:10.1038/s41598-025-08255-0 · Cited by 9 · see citing works
Yang, D., & Huang, X. (2024) · Crop Protection · doi:10.1016/j.cropro.2024.106620 · Cited by 9 · see citing works
Multi-Resolution-Based Deep Learning Approach for Rice Field Monitoring
Afaq, Y., & Manocha, A. (2022) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.2021.2010036 · Cited by 9 · see citing works
Multimodel Fusion Method for Cloud Detection in Satellite Laser Footprint Images
Tang, X., Yao, J., Chen, J., Li, G., & Zhang, W. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3192067 · Cited by 8 · see citing works
Wang, Z., Li, J., Zhang, T., & Yuan, S. (2022) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-022-01680-x · Cited by 8 · see citing works
Densely multiscale framework for segmentation of high resolution remote sensing imagery
Bello, I. M., Zhang, K., Su, Y., Wang, J., & Aslam, M. A. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105196 · Cited by 8 · see citing works
Gui, B., Bhardwaj, A., & Sam, L. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112702 · Cited by 7 · see citing works
Ren, B., Liu, B., Wang, Q., Hou, B., Yang, C., & Jiao, L. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3609898 · Cited by 7 · see citing works
Chang, Y., Lin, C., Dmitriev, A. V., Hsieh, M., Hsu, H., Lin, Y.-C., Mendoza, M. M., Huang, G., Tsai, L., Li, Y., et al. (2022) · Sensors · doi:10.3390/s22072758 · Cited by 7 · see citing works
Padilla-Zepeda, E., Torres-Román, D., & Méndez-Vázquez, A. (2023) · Remote Sensing · doi:10.3390/rs15051399 · Cited by 7 · see citing works
Song, D., Yang, C., Wang, B., Zhang, J., Gao, H., & Tang, Y. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3301310 · Cited by 7 · see citing works
An agricultural leaf disease segmentation model applying multi-scale coordinate attention mechanism
Gu, R., & Liu, L. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112904 · Cited by 6 · see citing works
Elgendy, A., Morgan, H., Tran, B., Thomas, R., Ismail, T., Shehadeh, Y. K., ElGharib, A., Al-Dughairi, A. A., Mubarak, A. E., Harhash, K. A., et al. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103534 · Cited by 6 · see citing works
RSImageNet: A Universal Deep Semantic Segmentation Lifecycle for Remote Sensing Images
Qin, Y., & Chi, M. (2020) · IEEE Access · doi:10.1109/access.2020.2986514 · Cited by 6 · see citing works
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 · see citing works
Hrlinknet: Linknet with High-Resolution Representation for High-Resolution Satellite Imagery
Wu, M., Shu, Z., Zhang, J., & Hu, X. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9554601 · Cited by 6 · see citing works
Jiang, T., Huang, Y., Hu, C., & Li, L. (2024) · Advances in Structural Engineering · doi:10.1177/13694332241298017 · Cited by 4 · see citing works
Zhu, K., Du, Y., Wang, Q., Ji, N., & Zhang, L. (2020) · IEEE Access · doi:10.1109/access.2020.3035166 · Cited by 4 · see citing works
Scattered Mountainous Area Building Extraction From an Open Satellite Imagery Dataset
Deng, S., Wu, S., Bian, A., Zhang, J., Di, B., Nienkötter, A., Deng, T., & Feng, T. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3247620 · Cited by 4 · see citing works
Li, L., Han, L., Miao, Q., Zhang, Y., & Jing, Y. (2022) · Land · doi:10.3390/land11112028 · Cited by 3 · see citing works
Remote sensing image classification method based on improved ShuffleNet convolutional neural network
Li, Z., Su, Y., Zhang, Y., Yin, H.-F., Sun, J., & Wu, X. (2024) · Intelligent Data Analysis · doi:10.3233/ida-227217 · Cited by 3 · see citing works
Qu, L., Song, X., Zhang, M., Wang, J., Wen, R., & Wang, S. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-8743-2_5 · Cited by 3 · see citing works
Nigar, A., Li, Y., Baloch, M. Y. J., Shafi, S., & Usman, A. G. (2026) · Environmental Monitoring and Assessment · doi:10.1007/s10661-026-15558-w · Cited by 2 · see citing works
Bello, I. M., Zhang, K., Wang, J., & Aslam, M. A. (2021) · IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS) · doi:10.1109/agers53903.2021.9617409 · Cited by 2 · see citing works
Research on 3D Reconstruction Technology of Power Line Based on Image Semantic Segmentation
Li, X., Zhang, F., Li, G., Xiang, D., & Yang, C. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2095/1/012015 · Cited by 2 · see citing works
Thamaraikannan, N., & Manju, S. (2025) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi64620.2025.10883139 · Cited by 1 · see citing works
Zhan, Z., Han, C., Zou, L., Dong, J., Yan, S., & Wang, W. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02588-3 · Cited by 1 · see citing works
Yu, L., Zhang, X., & Wang, K. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3361555 · Cited by 1 · see citing works
BACS: Background Aware Continual Semantic Segmentation
ElAraby, M., Harakeh, A., & Paull, L. (2024) · doi:10.21428/d82e957c.0941aa68 · Cited by 1 · see citing works
Li, L., Deng, Z., Zhang, B., Liu, Z., Wang, J., Bian, L., & Yang, C. (2022) · Geocarto International · doi:10.1080/10106049.2022.2138984 · Cited by 1 · see citing works
News Cover Assessment via Multi-task LearningPreprint
Sun, Z., Zhao, S., Zhu, C., & Chen, X. D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.07581
Research on Image Recognition of Machine Tool Based on Semantic Segmentation
Xu, K., Fei, Z., Sun, Y., & Ming, H. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1646/1/012147
Shuai, 袁. Y., Sun, Y., Weifeng, 何. H., & Shikui, 涂. T. (2023) · Laser & Optoelectronics Progress · doi:10.3788/lop213289
Saipullah, K. M., Saad, W. H. M., Wong, Q. L., HUSNI, M. S. B. M., & Razak, M. S. J. A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4524939
Research on Infrared Image Segmentation of Substation Arrester Based on DeepLabv3+
Zeng, C., Xie, J., Li, Z., Zou, J., Jin, S., & Cao, Y. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-1068-3_69
Zhao, S., Wang, L., Song, L., Ma, P., Liao, L., Liu, Z., & Zhao, X. (2024) · Frontiers in Marine Science · doi:10.3389/fmars.2024.1455604
A Modified Fully Convolutional Segmentation Network for Hyperspectral Image Classification
Faisal, M. F., Mallick, K., & Ahmed, B. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11021893
Adaptive Select Loss Strategy for Semantic Segmentation of Agricultural Crop Images
Florea, C., Florea, L., & Ivanovici, M. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3589635
Anand, R. (2026) · SN Computer Science · doi:10.1007/s42979-025-04678-8
An Integrated U-Net and Markov Random Field Approach for Pavement Crack Segmentation
Yuan, S.-C., Liu, D.-Z., Shi, W.-P., Li, P., Li, Y.-Q., & Yi, Y. (2026) · Applied Geophysics · doi:10.1007/s11770-026-1455-5
GFS-Net: Geometry–frequency synergistic fusion for accurate optical–SAR land-cover classification
Tao, H., Huang, Q., Zhong, T., Yang, Z., Qiu, H., & Jiang, C. (2026) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2026.105549
- Authors
- Niu, Z., Liu, W., Zhao, J., & Jiang, G.
- Affiliations
- Yanshan University (CN)
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2019-02
- Volume
- 16
- Issue
- 2
- Pages
- 251-255
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2894561647
- Cited by
- 86
- References
- 22
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 17 · 2023: 10 · 2022: 14 · 2021: 12 · 2020: 16 · 2019: 2
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Geoscience and Remote Sensing Letters (publishedVersion)
- Funders
- National Natural Science Foundation of China (51675461, 11673040, 61803329)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- hyperspectral image classification, DeepLab, spatial feature extraction, semantic segmentation, multi-scale feature extraction, spectral-spatial fusion, weighted fusion, support vector machine, spatial resolution preservation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
