{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T05:49:20Z","timestamp":1769752160285,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42071303"],"award-info":[{"award-number":["42071303"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFD1300505"],"award-info":[{"award-number":["2021YFD1300505"]}]},{"name":"National Natural Science Foundation of China","award":["KZ202110028044"],"award-info":[{"award-number":["KZ202110028044"]}]},{"name":"National Natural Science Foundation of China","award":["2022-NK-136"],"award-info":[{"award-number":["2022-NK-136"]}]},{"name":"National Key Research and Development Program of China","award":["42071303"],"award-info":[{"award-number":["42071303"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFD1300505"],"award-info":[{"award-number":["2021YFD1300505"]}]},{"name":"National Key Research and Development Program of China","award":["KZ202110028044"],"award-info":[{"award-number":["KZ202110028044"]}]},{"name":"National Key Research and Development Program of China","award":["2022-NK-136"],"award-info":[{"award-number":["2022-NK-136"]}]},{"name":"Joint program of Beijing Municipal Education Commission and Beijing Municipal Natural Science Foundation of China","award":["42071303"],"award-info":[{"award-number":["42071303"]}]},{"name":"Joint program of Beijing Municipal Education Commission and Beijing Municipal Natural Science Foundation of China","award":["2021YFD1300505"],"award-info":[{"award-number":["2021YFD1300505"]}]},{"name":"Joint program of Beijing Municipal Education Commission and Beijing Municipal Natural Science Foundation of China","award":["KZ202110028044"],"award-info":[{"award-number":["KZ202110028044"]}]},{"name":"Joint program of Beijing Municipal Education Commission and Beijing Municipal Natural Science Foundation of China","award":["2022-NK-136"],"award-info":[{"award-number":["2022-NK-136"]}]},{"name":"Science and Technology Program of Qinghai Province of China","award":["42071303"],"award-info":[{"award-number":["42071303"]}]},{"name":"Science and Technology Program of Qinghai Province of China","award":["2021YFD1300505"],"award-info":[{"award-number":["2021YFD1300505"]}]},{"name":"Science and Technology Program of Qinghai Province of China","award":["KZ202110028044"],"award-info":[{"award-number":["KZ202110028044"]}]},{"name":"Science and Technology Program of Qinghai Province of China","award":["2022-NK-136"],"award-info":[{"award-number":["2022-NK-136"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing images (RSIs) are widely used in various fields due to their versatility, accuracy, and capacity for earth observation. Direct application of RSIs to harvest optimal results is generally difficult, especially for weak information features in the images. Thus, extracting the weak information in RSIs is reasonable to promote further applications. However, the current techniques for weak information extraction mainly focus on spectral features in hyperspectral images (HSIs), and a universal weak information extraction technology for RSI is lacking. Therefore, this study focused on mining the weak information from RSIs and proposed the deep multi-order spatial\u2013spectral residual feature extractor (DMSRE). The DMSRE considers the global information and three-dimensional cube structures by combining low-rank representation, high-order residual quantization, and multi-granularity spectral segmentation theories. This extractor obtains spatial\u2013spectral features from two derived sequences (deep spatial\u2013spectral residual feature (DMSR) and deep spatial\u2013spectral coding feature (DMSC)), and three RSI datasets (i.e., Chikusei, ZY1-02D, and Pasture datasets) were employed to validate the DMSRE method. Comparative results of the weak information extraction-based classifications (including DMSR and DMSC) and the raw image-based classifications showed the following: (i) the DMSRs can improve the classification accuracy of individual classes in fine classification applications (e.g., Asphalt class in the Chikusei dataset, from 89.12% to 95.99%); (ii) the DMSC improved the overall accuracy in rough classification applications (from 92.07% to 92.78%); and (iii) the DMSC improved the overall accuracy in RGB classification applications (from 63.25% to 63.6%), whereas DMSR improved the classification accuracy of individual classes on the RGB image (e.g., Plantain classes in the Pasture dataset, from 32.49% to 39.86%). This study demonstrates the practicality and capability of the DMSRE method to promote target recognition on RSIs and presents an alternative technique for weak information mining on RSIs, indicating the potential to extend weak information-based applications of RSIs.<\/jats:p>","DOI":"10.3390\/rs16111957","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T03:45:08Z","timestamp":1717040708000},"page":"1957","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Multi-Order Spatial\u2013Spectral Residual Feature Extractor for Weak Information Mining in Remote Sensing Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9394-0461","authenticated-orcid":false,"given":"Xizhen","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Spatial Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aiwu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Spatial Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Sun","sequence":"additional","affiliation":[{"name":"Aerosphere Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Spatial Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyang","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Ecology, Resources and Environment, Dezhou University, Dezhou 253023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinbang","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Spatial Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, 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Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11058","DOI":"10.1109\/JSTARS.2021.3123080","article-title":"Multilayer Feature Extraction Network for Military Ship Detection from High-Resolution Optical Remote Sensing Images","volume":"14","author":"Qin","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112750","DOI":"10.1016\/j.rse.2021.112750","article-title":"A Review of Machine Learning in Processing Remote Sensing Data for Mineral Exploration","volume":"268","author":"Shirmard","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"5607514","article-title":"Remote Sensing Image Change Detection with Transformers","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.3390\/rs2092274","article-title":"Remote Sensing of Irrigated Agriculture: Opportunities and Challenges","volume":"2","author":"Ozdogan","year":"2010","journal-title":"Remote Sens."},{"key":"ref_6","unstructured":"Yu, S., De Backer, S., and Scheunders, P. (2000, January 8\u201311). Genetic Feature Selection Combined with Composite Fuzzy Nearest Neighbor Classifiers for High-Dimensional Remote Sensing Data. Proceedings of the SMC 2000 International Conference on Systems, Man and Cybernetics. \u201cCybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions\u201d (Cat. No.00CH37166), Nashville, TN, USA."},{"key":"ref_7","unstructured":"Bhuvaneswari, K., Dhamotharan, R., and Radhakrishnan, N. (2011). Information Extraction from Remote Sensing Image (RSI) for a Coastal Environment Along a Selected Coastline of Tamilnadu. IJCSET Board Memb., 95."},{"key":"ref_8","first-page":"102966","article-title":"A Context-Scale-Aware Detector and a New Benchmark for Remote Sensing Small Weak Object Detection in Unmanned Aerial Vehicle Images","volume":"112","author":"Han","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sun, Y., Cai, W., and Shao, X. (2022). Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy. Molecules, 27.","DOI":"10.3390\/molecules27020452"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fan, X., Kang, X., Gao, P., Zhang, Z., Wang, J., Zhang, Q., Zhang, M., Ma, L., Lv, X., and Zhang, L. (2023). Soil Salinity Estimation in Cotton Fields in Arid Regions Based on Multi-Granularity Spectral Segmentation (MGSS). Remote Sens., 15.","DOI":"10.3390\/rs15133358"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.iswcr.2023.03.002","article-title":"Remote Sensing of Soil Degradation: Progress and Perspective","volume":"11","author":"Wang","year":"2023","journal-title":"Int. Soil Water Conserv. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., and Alamri, A. (2020). Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review. Remote Sens., 12.","DOI":"10.3390\/rs12091444"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MGRS.2020.2979764","article-title":"Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep: Overview and Toolbox","volume":"8","author":"Rasti","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_14","unstructured":"Ebied, H.M. (2012, January 14\u201316). Feature Extraction Using PCA and Kernel-PCA for Face Recognition. Proceedings of the 2012 8th International Conference on Informatics and Systems (INFOS), Giza, Egypt."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1016\/j.patcog.2014.12.012","article-title":"Fast Incremental LDA Feature Extraction","volume":"48","author":"Rudzicz","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/LGRS.2008.2001282","article-title":"Limitations of Principal Components Analysis for Hyperspectral Target Recognition","volume":"5","author":"Prasad","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6248","DOI":"10.1080\/01431161.2020.1736732","article-title":"Feature Extraction for Hyperspectral Image Classification: A Review","volume":"41","author":"Kumar","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.11834\/jig.210198","article-title":"Review of Spatial-Spectral Feature Extraction for Hyperspectral Image","volume":"26","author":"Ye","year":"2021","journal-title":"J. Image Graph."},{"key":"ref_19","first-page":"1","article-title":"Robust Principal Component Analysis?","volume":"58","author":"Li","year":"2011","journal-title":"J. ACM"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1198\/106186006X113430","article-title":"Sparse Principal Component Analysis","volume":"15","author":"Zou","year":"2006","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/S0031-3203(02)00048-1","article-title":"Why Can LDA Be Performed in PCA Transformed Space?","volume":"36","author":"Yang","year":"2003","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/TGRS.2008.2005729","article-title":"Classification of Hyperspectral Images with Regularized Linear Discriminant Analysis","volume":"47","author":"Bandos","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1109\/LGRS.2007.900751","article-title":"Modified Fisher\u2019s Linear Discriminant Analysis for Hyperspectral Imagery","volume":"4","author":"Du","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1111\/1467-9868.00196","article-title":"Probabilistic Principal Component Analysis","volume":"61","author":"Tipping","year":"1999","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.neucom.2012.12.007","article-title":"Normalized Discriminant Analysis for Dimensionality Reduction","volume":"110","author":"Liang","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_26","unstructured":"He, X., and Niyogi, P. (2003). Locality Preserving Projections. Adv. Neural Inf. Process. Syst., 16."},{"key":"ref_27","unstructured":"Cai, D., He, X., Wang, X., Bao, H., and Han, J. (2009, January 11\u201317). Locality Preserving Nonnegative Matrix Factorization. Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, Pasadena, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the Parts of Objects by Non-Negative Matrix Factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3386","DOI":"10.1109\/TGRS.2006.880626","article-title":"Structured Gaussian Components for Hyperspectral Image Classification","volume":"44","author":"Berge","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3706","DOI":"10.1109\/TGRS.2006.881741","article-title":"Modeling and Detection of Geospatial Objects Using Texture Motifs","volume":"44","author":"Bhagavathy","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/36.905239","article-title":"A New Approach for the Morphological Segmentation of High-Resolution Satellite Imagery","volume":"39","author":"Pesaresi","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1109\/TGRS.2003.817269","article-title":"A Markov Random Field Approach to Spatio-Temporal Contextual Image Classification","volume":"41","author":"Melgani","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1109\/TGRS.2008.916644","article-title":"Automatic Detection of Geospatial Objects Using Multiple Hierarchical Segmentations","volume":"46","author":"Akcay","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.isprsjprs.2007.08.007","article-title":"Object-Based Classification Using Quickbird Imagery for Delineating Forest Vegetation Polygons in a Mediterranean Test Site","volume":"63","author":"Mallinis","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Advances in Spectral-Spatial Classification of Hyperspectral Images","volume":"101","author":"Fauvel","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of Hyperspectral Data from Urban Areas Based on Extended Morphological Profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3742","DOI":"10.1109\/TGRS.2013.2275613","article-title":"Feature Extraction of Hyperspectral Images with Image Fusion and Recursive Filtering","volume":"52","author":"Kang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral\u2013Spatial Classification of Hyperspectral Data Based on Deep Belief Network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4073","DOI":"10.1109\/JSTARS.2016.2517204","article-title":"Spectral\u2013Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder","volume":"9","author":"Ma","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"110760","DOI":"10.1016\/j.measurement.2022.110760","article-title":"Performance Evaluation of Deep E-CNN with Integrated Spatial-Spectral Features in Hyperspectral Image Classification","volume":"191","author":"Kavitha","year":"2022","journal-title":"Measurement"},{"key":"ref_43","first-page":"3013","article-title":"A Novel Method for High-Order Residual Quantization-Based Spectral Binary Coding","volume":"39","author":"Kang","year":"2019","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_44","first-page":"161","article-title":"Hyperspectral Remote Sensing Estimation of Pasture Crude Protein Content Based on Multi-Granularity Spectral Feature","volume":"35","author":"Kang","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Eng"},{"key":"ref_45","first-page":"250","article-title":"Estimation of Grassland Aboveground Biomass from UAV-Mounted Hyperspectral Image by Optimized Spectral Reconstruction","volume":"41","author":"Kang","year":"2021","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Pang, H., Zhang, A., Yin, S., Zhang, J., Dong, G., He, N., Qin, W., and Wei, D. (2022). Estimating Carbon, Nitrogen, and Phosphorus Contents of West\u2013East Grassland Transect in Inner Mongolia Based on Sentinel-2 and Meteorological Data. Remote Sens., 14.","DOI":"10.3390\/rs14020242"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, Z., Ni, B., Zhang, W., Yang, X., and Gao, W. (2017, January 22\u201329). Performance Guaranteed Network Acceleration via High-Order Residual Quantization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.282"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A. (2016). Xnor-Net: Imagenet Classification Using Binary Convolutional Neural Networks, Springer.","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref_49","unstructured":"Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., and Bengio, Y. (2016). Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or \u22121. arXiv."},{"key":"ref_50","unstructured":"Liu, G., Lin, Z., and Yu, Y. (2010, January 21\u201324). Robust Subspace Segmentation by Low-Rank Representation. Proceedings of the 27th international conference on machine learning (ICML-10), Haifa, Israel."},{"key":"ref_51","unstructured":"Yokoya, N., and Iwasaki, A. (2016). Airborne Hyperspectral Data over Chikusei, Space Application Laboratory, The University of Tokyo. Technical Report SAL-2016-05-27."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, A., Portelli, R., Zhang, X., and Guan, H. (2022). ZY-1 02D Hyperspectral Imagery Super-Resolution via Endmember Matrix Constraint Unmixing. Remote Sens., 14.","DOI":"10.3390\/rs14164034"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1109\/JSTARS.2019.2896031","article-title":"Hyperspectral Image Denoising via Subspace-Based Nonlocal Low-Rank and Sparse Factorization","volume":"12","author":"Cao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8","DOI":"10.4236\/jcc.2019.73002","article-title":"Image Quality Assessment through FSIM, SSIM, MSE and PSNR\u2014A Comparative Study","volume":"7","author":"Sara","year":"2019","journal-title":"J. Comput. Commun."},{"key":"ref_55","unstructured":"Zhang, X., Schaaf, C.B., Friedl, M.A., Strahler, A.H., Gao, F., and Hodges, J.C.F. (2002, January 24\u201328). MODIS Tasseled Cap Transformation and Its Utility. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3926","DOI":"10.1038\/s41598-024-54308-1","article-title":"JAXA\u2019s New High-Resolution Land Use Land Cover Map for Vietnam Using a Time-Feature Convolutional Neural Network","volume":"14","author":"Truong","year":"2024","journal-title":"Sci. Rep."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1957\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:50:25Z","timestamp":1760107825000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,29]]},"references-count":56,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16111957"],"URL":"https:\/\/doi.org\/10.3390\/rs16111957","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,29]]}}}