{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:20:03Z","timestamp":1772205603448,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T00:00:00Z","timestamp":1720396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021ZD0112902"],"award-info":[{"award-number":["2021ZD0112902"]}]},{"name":"National Key Research and Development Program of China","award":["62272375"],"award-info":[{"award-number":["62272375"]}]},{"name":"National Key Research and Development Program of China","award":["12226004"],"award-info":[{"award-number":["12226004"]}]},{"name":"China NSFC Projects","award":["2021ZD0112902"],"award-info":[{"award-number":["2021ZD0112902"]}]},{"name":"China NSFC Projects","award":["62272375"],"award-info":[{"award-number":["62272375"]}]},{"name":"China NSFC Projects","award":["12226004"],"award-info":[{"award-number":["12226004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the intricate terrain and restricted resources, hyperspectral image (HSI) datasets captured in real-world scenarios typically contain noisy labels, which may seriously affect the classification results. To address this issue, we work on a universal method that rectifies the labels first and then trains the classifier with corrected labels. In this study, we relax the common assumption that all training data are potentially corrupted and instead posit the presence of a small set of reliable data points within the training set. Under this framework, we propose a novel label-correction method named adaptive selective loss propagation algorithm (ASLPA). Firstly, the spectral\u2013spatial information is extracted from the hyperspectral image and used to construct the inter-pixel transition probability matrix. Secondly, we construct the trusted set with the known clean data and estimate the proportion of accurate labels within the untrusted set. Then, we enlarge the trusted set according to the estimated proportion and identify an adaptive number of samples with lower loss values from the untrusted set to supplement the trusted set. Finally, we conduct label propagation based on the enlarged trusted set. This approach takes full advantage of label information from the trusted and untrusted sets, and moreover the exploitation on the untrusted set can adjust adaptively according to the estimated noise level. Experimental results on three widely used HSI datasets show that our proposed ASLPA method performs better than the state-of-the-art label-cleaning methods.<\/jats:p>","DOI":"10.3390\/rs16132499","type":"journal-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T11:30:02Z","timestamp":1720438202000},"page":"2499","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Adaptive Noisy Label-Correction Method Based on Selective Loss for Hyperspectral Image-Classification Problem"],"prefix":"10.3390","volume":"16","author":[{"given":"Zina","family":"Li","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Xiaorui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Statistics and Data Science, Nankai University, Tianjin 300072, China"}]},{"given":"Deyu","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Xiangyong","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Automation, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2632","DOI":"10.1109\/TGRS.2012.2216272","article-title":"Tree species classification in boreal forests with hyperspectral data","volume":"51","author":"Dalponte","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liang, L., Di, L., Huang, T., Wang, J., Lin, L., Wang, L., and Yang, M. (2018). Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm. Remote Sens., 10.","DOI":"10.3390\/rs10121940"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Berger, K., Atzberger, C., Danner, M., Wocher, M., Mauser, W., and Hank, T. (2018). Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model. Remote Sens., 10.","DOI":"10.3390\/rs10122063"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yokoya, N., Chan, J.C.W., and Segl, K. (2016). Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images. Remote Sens., 8.","DOI":"10.3390\/rs8030172"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6076","DOI":"10.1109\/TGRS.2016.2580702","article-title":"Robust Collaborative Nonnegative Matrix Factorization for Hyperspectral Unmixing","volume":"54","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1109\/JSTARS.2014.2319585","article-title":"Spectral unmixing-based crop residue estimation using hyperspectral remote sensing data: A case study at Purdue university","volume":"7","author":"Chi","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1109\/TGRS.2014.2326654","article-title":"Fast hyperspectral anomaly detection via high-order 2-D crossing filter","volume":"53","author":"Yuan","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Advances in spectral\u2013spatial classification of hyperspectral images","volume":"101","author":"Fauvel","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.1109\/TGRS.2015.2445767","article-title":"Classification of hyperspectral images by exploiting spectral\u2013spatial information of superpixel via multiple kernels","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4346","DOI":"10.1109\/TGRS.2018.2815588","article-title":"Decolorization-based hyperspectral image visualization","volume":"56","author":"Kang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5406519","DOI":"10.1109\/TGRS.2023.3311622","article-title":"Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images","volume":"61","author":"Tarasiewicz","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"417","DOI":"10.2174\/1573405618666220519144358","article-title":"Hyperspectral imaging: A review and trends towards medical imaging","volume":"19","author":"Karim","year":"2023","journal-title":"Curr. Med. Imaging"},{"key":"ref_13","first-page":"5609318","article-title":"RGB-to-HSV: A Frequency-Spectrum Unfolding Network for Spectral Super-Resolution of RGB Videos","volume":"62","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pelletier, C., Valero, S., Inglada, J., Champion, N., Marais Sicre, C., and Dedieu, G. (2017). Effect of training class label noise on classification performances for land cover mapping with satellite image time series. Remote Sens., 9.","DOI":"10.3390\/rs9020173"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7195","DOI":"10.1109\/TGRS.2018.2849225","article-title":"The effect of ground truth on performance evaluation of hyperspectral image classification","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.patrec.2019.11.022","article-title":"Hyperspectral anomaly detection via density peak clustering","volume":"129","author":"Tu","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_17","first-page":"4099","article-title":"Local manifold learning-based k-nearest-neighbor for hyperspectral image classification","volume":"48","author":"Ma","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1109\/ACCESS.2017.2669149","article-title":"Weighted generalized nearest neighbor for hyperspectral image classification","volume":"5","author":"Bo","year":"2017","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Demir, B., and Ert\u00fcrk, S. (2009, January 7\u201310). Improving SVM classification accuracy using a hierarchical approach for hyperspectral images. Proceedings of the 2009 16th IEEE International Conference on Image Processing (ICIP), Cairo, Egypt.","DOI":"10.1109\/ICIP.2009.5414491"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","article-title":"Investigation of the random forest framework for classification of hyperspectral data","volume":"43","author":"Ham","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/LGRS.2013.2254108","article-title":"Hyperspectral remote sensing image classification based on rotation forest","volume":"11","author":"Xia","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/TGRS.2017.2744662","article-title":"Random forest ensembles and extended multiextinction profiles for hyperspectral image classification","volume":"56","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Maschler, J., Atzberger, C., and Immitzer, M. (2018). Individual tree crown segmentation and classification of 13 tree species using airborne hyperspectral data. Remote Sens., 10.","DOI":"10.3390\/rs10081218"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1109\/JSTARS.2014.2301775","article-title":"E2LMs: Ensemble Extreme Learning Machines for Hyperspectral Image Classification","volume":"7","author":"Samat","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2014.2381602","article-title":"Local binary patterns and extreme learning machine for hyperspectral imagery classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TGRS.2012.2201730","article-title":"Hyperspectral image classification via kernel sparse representation","volume":"51","author":"Chen","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/LGRS.2017.2787338","article-title":"Hyperspectral image classification via fusing correlation coefficient and joint sparse representation","volume":"15","author":"Tu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1109\/TGRS.2009.2037898","article-title":"Semisupervised neural networks for efficient hyperspectral image classification","volume":"48","author":"Ratle","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TGRS.2018.2794326","article-title":"Hyperspectral image classification with deep feature fusion network","volume":"56","author":"Song","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2666","DOI":"10.1109\/TGRS.2013.2264508","article-title":"Spectral\u2013spatial hyperspectral image classification with edge-preserving filtering","volume":"52","author":"Kang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/TGRS.2017.2765364","article-title":"Recent advances on spectral\u2013spatial hyperspectral image classification: An overview and new guidelines","volume":"56","author":"He","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.neucom.2018.07.052","article-title":"Spatial-spectral classification of hyperspectral image via group tensor decomposition","volume":"316","author":"Zhao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., and Belongie, S. (2017, January 21\u201326). Learning from Noisy Large-Scale Datasets with Minimal Supervision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.696"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., and Li, L.J. (2017, January 22\u201329). Learning from Noisy Labels with Distillation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.211"},{"key":"ref_36","unstructured":"Ren, M., Zeng, W., Yang, B., and Urtasun, R. (2018, January 10\u201315). Learning to Reweight Examples for Robust Deep Learning. Proceedings of the International Conference on Machine Learning, PMLR, Stockholm, Sweden."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Charikar, M., Steinhardt, J., and Valiant, G. (2017, January 19\u201323). Learning from Untrusted Data. Proceedings of the Annual ACM SIGACT Symposium on Theory of Computing, Montreal, QC, Canada.","DOI":"10.1145\/3055399.3055491"},{"key":"ref_38","unstructured":"Hendrycks, D., Mazeika, M., Wilson, D., and Gimpel, K. (2018, January 3\u20138). Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/TNNLS.2020.3029523","article-title":"Multilayer Spectral\u2013Spatial Graphs for Label Noisy Robust Hyperspectral Image Classification","volume":"33","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_40","first-page":"5502511","article-title":"Dual-channel residual network for hyperspectral image classification with noisy labels","volume":"60","author":"Xu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1109\/TGRS.2018.2867444","article-title":"Density Peak-Based Noisy Label Detection for Hyperspectral Image Classification","volume":"57","author":"Tu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tu, B., Zhou, C., Peng, J., He, W., Ou, X., and Xu, Z. (2019). Kernel entropy component analysis-based robust hyperspectral image supervised classification. Remote Sens., 11.","DOI":"10.3390\/rs11232823"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1109\/JSTARS.2020.2994162","article-title":"Hierarchical Structure-Based Noisy Labels Detection for Hyperspectral Image Classification","volume":"13","author":"Tu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pelletier, C., Valero, S., Inglada, J., Dedieu, G., and Champion, N. (2017, January 27\u201329). Filtering mislabeled data for improving time series classification. Proceedings of the 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, Brugge, Belgium.","DOI":"10.1109\/Multi-Temp.2017.8035217"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1109\/TGRS.2018.2861992","article-title":"Hyperspectral Image Classification in the Presence of Noisy Labels","volume":"57","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/TNNLS.2015.2477537","article-title":"Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking","volume":"27","author":"Wang","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep Recurrent Neural Networks for Hyperspectral Image Classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral Image Classification Using Deep Pixel-Pair Features","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","article-title":"Deep pyramidal residual networks for spectral\u2013spatial hyperspectral image classification","volume":"57","author":"Paoletti","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, L., Peng, J., and Sun, W. (2019). Spatial\u2013spectral squeeze-and-excitation residual network for hyperspectral image classification. Remote Sens., 11.","DOI":"10.3390\/rs11070884"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3232","DOI":"10.1109\/TGRS.2019.2951160","article-title":"Spectral\u2013Spatial Attention Network for Hyperspectral Image Classification","volume":"58","author":"Sun","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1109\/TGRS.2017.2698503","article-title":"Learning and Transferring Deep Joint Spectral\u2013Spatial Features for Hyperspectral Classification","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TGRS.2017.2756851","article-title":"Multisource Remote Sensing Data Classification Based on Convolutional Neural Network","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TGRS.2020.3007921","article-title":"Hyperspectral Image Classification With Attention-Aided CNNs","volume":"59","author":"Hang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Liu, D., Han, G., Liu, P., Yang, H., Chen, D., Li, Q., Wu, J., and Wang, Y. (2022). A discriminative spectral\u2013spatial-semantic feature network based on shuffle and frequency attention mechanisms for hyperspectral image classification. Remote Sens., 14.","DOI":"10.3390\/rs14112678"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Liu, D., Shao, T., Qi, G., Li, M., and Zhang, J. (2023). A Hybrid-Scale Feature Enhancement Network for Hyperspectral Image Classification. Remote Sens., 16.","DOI":"10.3390\/rs16010022"},{"key":"ref_57","first-page":"5509705","article-title":"Lightweight heterogeneous kernel convolution for hyperspectral image classification with noisy labels","volume":"19","author":"Roy","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_58","first-page":"500116","article-title":"Triple Contrastive Representation Learning for Hyperspectral Image Classification with Noisy Labels","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","first-page":"5505514","article-title":"Attentive-Adaptive Network for Hyperspectral Images Classification with Noisy Labels","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, J., Shi, H., Ge, Z., Yu, Q., Cao, G., and Li, X. (2023). Agreement and Disagreement-Based Co-Learning with Dual Network for Hyperspectral Image Classification with Noisy Labels. Remote Sens., 15.","DOI":"10.3390\/rs15102543"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"5085","DOI":"10.1109\/TGRS.2019.2896471","article-title":"Spatial Density Peak Clustering for Hyperspectral Image Classification with Noisy Labels","volume":"57","author":"Tu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"4116","DOI":"10.1109\/TGRS.2019.2961141","article-title":"Hyperspectral Classification with Noisy Label Detection via Superpixel-to-Pixel Weighting Distance","volume":"58","author":"Tu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Leng, Q., Yang, H., and Jiang, J. (2019). Label noise cleansing with sparse graph for hyperspectral image classification. Remote Sens., 11.","DOI":"10.3390\/rs11091116"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Liu, M.Y., Tuzel, O., Ramalingam, S., and Chellappa, R. (2011, January 20\u201325). Entropy rate superpixel segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995323"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Canny, J. (1987). A Computational Approach to Edge Detection. Readings in Computer Vision, Elsevier.","DOI":"10.1016\/B978-0-08-051581-6.50024-6"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4940","DOI":"10.1109\/JSTARS.2019.2941454","article-title":"Hyperspectral band selection via adaptive subspace partition strategy","volume":"12","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2499\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:11:46Z","timestamp":1760109106000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2499"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,8]]},"references-count":68,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132499"],"URL":"https:\/\/doi.org\/10.3390\/rs16132499","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,8]]}}}