{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:50:29Z","timestamp":1760147429025,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61971233","62076137","61901191","ZR2020LZH005","2022M713668"],"award-info":[{"award-number":["61971233","62076137","61901191","ZR2020LZH005","2022M713668"]}]},{"name":"Shangdong Provincial Natural Science Foundation","award":["61971233","62076137","61901191","ZR2020LZH005","2022M713668"],"award-info":[{"award-number":["61971233","62076137","61901191","ZR2020LZH005","2022M713668"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["61971233","62076137","61901191","ZR2020LZH005","2022M713668"],"award-info":[{"award-number":["61971233","62076137","61901191","ZR2020LZH005","2022M713668"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spectral unmixing is among one of the major hyperspectral image analysis tasks that aims to extract basic features (endmembers) at the subpixel level and estimate their corresponding proportions (fractional abundances). Recently, the rapid development of deep learning networks has provided us with a new method to solve the problem of spectral unmixing. In this paper, we propose a spatial-information-assisted spectral information learning unmixing network (SISLU-Net) for hyperspectral images. The SISLU-Net consists of two branches. The upper branch focuses on the extraction of spectral information. The input of the upper branch is a number of pixels randomly extracted from the hyperspectral image. The data are fed into the network as a random combination of different pixel blocks each time. The random combination of batches can boost the network to learn global spectral information. Another branch focuses on learning spatial information from the entire hyperspectral image and transmitting it to the upper branch through the shared weight strategy. This allows the network to take into account the spectral information and spatial information of HSI at the same time. In addition, according to the distribution characteristics of endmembers, we employ Wing loss to solve the problem of uneven distributions of endmembers. Experimental results on one synthetic and three real hyperspectral data sets show that SISLU-Net is effective and competitive compared with several state-of-the-art unmixing algorithms in terms of the spectral angle distance (SAD) of the endmembers and the root mean square error (RMSE) of the abundances.<\/jats:p>","DOI":"10.3390\/rs15030817","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T05:33:53Z","timestamp":1675229633000},"page":"817","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SISLU-Net: Spatial Information-Assisted Spectral Information Learning Unmixing Network for Hyperspectral Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6465-8678","authenticated-orcid":false,"given":"Le","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Ying","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Baozhu","family":"Li","sequence":"additional","affiliation":[{"name":"Internet of Things & Smart City Innovation Platform, Zhuhai Fudan Innovation Institute, Zhuhai 519031, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/S0169-5347(03)00070-3","article-title":"Remote sensing for biodiversity science and conservation","volume":"18","author":"Turner","year":"2003","journal-title":"Trends Ecol. Evol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MGRS.2021.3064051","article-title":"Interpretable hyperspectral artificial intelligence: When nonconvex modeling meets hyperspectral remote sensing","volume":"9","author":"Hong","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4045","DOI":"10.1109\/JSTARS.2022.3175191","article-title":"SPANet: Successive Pooling Attention Network for Semantic Segmentation of Remote Sensing Images","volume":"15","author":"Sun","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3231215","article-title":"Multi-structure KELM with attention fusion strategy for hyperspectral image classification","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.3390\/land11081222","article-title":"Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review","volume":"11","author":"Allan","year":"2022","journal-title":"Land"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.isprsjprs.2021.12.005","article-title":"Land-use\/land-cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery","volume":"184","author":"Zhu","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","first-page":"1","article-title":"Interior Attention-Aware Network for Infrared Small Target Detection","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111612","DOI":"10.1016\/j.measurement.2022.111612","article-title":"Application of Helbig integrals to magnetic gradient tensor multi-target detection","volume":"200","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5146","DOI":"10.1109\/TGRS.2019.2897139","article-title":"ORSIm detector: A novel object detection framework in optical remote sensing imagery using spatial-frequency channel features","volume":"57","author":"Wu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jin, Q., Ma, Y., Mei, X., Dai, X., Li, H., Fan, F., and Huang, J. (August, January 28). Gaussian mixture model for hyperspectral unmixing with low-rank representation. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898410"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jin, Q., Ma, Y., Mei, X., Li, H., and Ma, J. (2021, January 6\u201311). UTDN: An unsupervised two-stream Dirichlet-Net for hyperspectral unmixing. Proceedings of the ICASSP 2021\u20132021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414810"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches","volume":"5","author":"Plaza","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5167","DOI":"10.3390\/rs14205167","article-title":"Nonlinear Unmixing via Deep Autoencoder Networks for Generalized Bilinear Model","volume":"14","author":"Zhang","year":"2022","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/MSP.2013.2279731","article-title":"A signal processing perspective on hyperspectral unmixing: Insights from remote sensing","volume":"31","author":"Ma","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1109\/JSTARS.2017.2771482","article-title":"Integrating spatial information in the normalized P-linear algorithm for nonlinear hyperspectral unmixing","volume":"11","author":"Tang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2644","DOI":"10.1109\/JSTARS.2015.2427517","article-title":"Nonlinear hyperspectral unmixing using nonlinearity order estimation and polytope decomposition","volume":"8","author":"Marinoni","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5211","DOI":"10.1109\/TGRS.2019.2897430","article-title":"Improving reliability in nonlinear hyperspectral unmixing by multidimensional structural optimization","volume":"57","author":"Marinoni","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2005.844293","article-title":"Vertex component analysis: A fast algorithm to unmix hyperspectral data","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Winter, M.E. (1999, January 18\u201323). N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data. Proceedings of the SPIE\u2019s International Symposium on Optical Science, Engineering, and Instrumentation, Denver, CO, USA.","DOI":"10.1117\/12.366289"},{"key":"ref_20","unstructured":"Boardman, J., Kruscl, F., and Grccn, R. (1995). Mapping target signatures via partial unmixing of AVIRIS data. Summaries of the Fifth Annual JPL Airborne Earth Science Workshop, NASA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5067","DOI":"10.1109\/TGRS.2015.2417162","article-title":"Minimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1109\/TGRS.2011.2163941","article-title":"Hyperspectral unmixing based on mixtures of Dirichlet components","volume":"50","author":"Nascimento","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6019","DOI":"10.1109\/JSTARS.2020.3027155","article-title":"Efficient hyperspectral target detection and identification with large spectral libraries","volume":"13","author":"Loughlin","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bioucas-Dias, J.M., and Figueiredo, M.A. (2010, January 14\u201316). Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing. Proceedings of the 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Reykjavik, Iceland.","DOI":"10.1109\/WHISPERS.2010.5594963"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4484","DOI":"10.1109\/TGRS.2012.2191590","article-title":"Total variation spatial regularization for sparse hyperspectral unmixing","volume":"50","author":"Iordache","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/TGRS.2013.2240001","article-title":"Collaborative sparse regression for hyperspectral unmixing","volume":"52","author":"Iordache","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6854","DOI":"10.1109\/TGRS.2020.3030233","article-title":"Nonlocal tensor-based sparse hyperspectral unmixing","volume":"59","author":"Huang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6916","DOI":"10.1109\/TNNLS.2021.3083931","article-title":"Multilayer sparsity-based tensor decomposition for low-rank tensor completion","volume":"33","author":"Xue","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13887","DOI":"10.1109\/TCYB.2021.3140148","article-title":"When Laplacian Scale Mixture Meets Three-Layer Transform: A Parametric Tensor Sparsity for Tensor Completion","volume":"52","author":"Xue","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_30","first-page":"1","article-title":"Deep Autoencoder for Hyperspectral Unmixing via Global-Local Smoothing","volume":"60","author":"Xu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"1","article-title":"Hyperspectral Unmixing Using Transformer Network","volume":"60","author":"Ghosh","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","first-page":"1","article-title":"SSCU-Net: Spatial\u2013Spectral Collaborative Unmixing Network for Hyperspectral Images","volume":"60","author":"Qi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3032","DOI":"10.1016\/j.patcog.2009.04.008","article-title":"On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images","volume":"42","author":"Plaza","year":"2009","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4163","DOI":"10.1109\/TGRS.2011.2160950","article-title":"Pixel unmixing in hyperspectral data by means of neural networks","volume":"49","author":"Licciardi","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","unstructured":"Kong, F., Chen, M., Cao, T., and Meng, Y. (2022). Proceedings of the International Conference in Communications, Signal Processing, and Systems, Springer."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hong, D., Chanussot, J., Yokoya, N., Heiden, U., Heldens, W., and Zhu, X.X. (August, January 28). WU-Net: A weakly-supervised unmixing network for remotely sensed hyperspectral imagery. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899865"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jin, Q., Ma, Y., Fan, F., Huang, J., Mei, X., and Ma, J. (2021). Adversarial autoencoder network for hyperspectral unmixing. IEEE Trans. Neural Netw. Learn. Syst.","DOI":"10.1109\/TNNLS.2021.3114203"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3042202","article-title":"Hyperspectral unmixing for additive nonlinear models with a 3-D-CNN autoencoder network","volume":"60","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/TGRS.2018.2856929","article-title":"Endnet: Sparse autoencoder network for endmember extraction and hyperspectral unmixing","volume":"57","author":"Ozkan","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4309","DOI":"10.1109\/TGRS.2018.2890633","article-title":"DAEN: Deep autoencoder networks for hyperspectral unmixing","volume":"57","author":"Su","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TCI.2019.2948726","article-title":"Deep generative endmember modeling: An application to unsupervised spectral unmixing","volume":"6","author":"Borsoi","year":"2019","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1109\/TGRS.2018.2868690","article-title":"uDAS: An untied denoising autoencoder with sparsity for spectral unmixing","volume":"57","author":"Qu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1109\/LGRS.2020.3005999","article-title":"Autoencoder network for hyperspectral unmixing with adaptive abundance smoothing","volume":"18","author":"Hua","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1109\/LGRS.2018.2857804","article-title":"Hyperspectral unmixing via deep convolutional neural networks","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1109\/JSTARS.2020.2966512","article-title":"Hyperspectral unmixing using deep convolutional autoencoders in a supervised scenario","volume":"13","author":"Khajehrayeni","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TGRS.2020.2992743","article-title":"Convolutional autoencoder for spectral\u2013spatial hyperspectral unmixing","volume":"59","author":"Palsson","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","first-page":"1","article-title":"CyCU-Net: Cycle-consistency unmixing network by learning cascaded autoencoders","volume":"60","author":"Gao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","first-page":"1","article-title":"Misicnet: Minimum simplex convolutional network for deep hyperspectral unmixing","volume":"60","author":"Rasti","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Huang, Y., Li, J., Qi, L., Wang, Y., and Gao, X. (October, January 26). Spatial-spectral autoencoder networks for hyperspectral unmixing. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324696"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Feng, Z.H., Kittler, J., Awais, M., Huber, P., and Wu, X. (2018). Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks. Comput. Vis. Pattern Recognit., 2235\u20132245.","DOI":"10.1109\/CVPR.2018.00238"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"71909","DOI":"10.1109\/ACCESS.2021.3079243","article-title":"A serial-parallel self-attention network joint with multi-scale dilated convolution","volume":"9","author":"Gaihua","year":"2021","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1007\/s00371-020-01903-8","article-title":"(SARN) spatial-wise attention residual network for image super-resolution","volume":"37","author":"Shi","year":"2021","journal-title":"Vis. Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2242","DOI":"10.1109\/TIP.2018.2795744","article-title":"A Gaussian mixture model representation of endmember variability in hyperspectral unmixing","volume":"27","author":"Zhou","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5987","DOI":"10.1109\/TIP.2016.2618002","article-title":"A spatial compositional model for linear unmixing and endmember uncertainty estimation","volume":"25","author":"Zhou","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Davis, C.O., Kavanaugh, M., Letelier, R., Bissett, W.P., and Kohler, D. (2007, January 19\u201320). Spatial and spectral resolution considerations for imaging coastal waters. Proceedings of the Optical Engineering + Applications, San Diego, CA, USA.","DOI":"10.1117\/12.734288"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/36.911111","article-title":"Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery","volume":"39","author":"Heinz","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"9858","DOI":"10.1109\/TGRS.2019.2929776","article-title":"Regularization parameter selection in minimum volume hyperspectral unmixing","volume":"57","author":"Zhuang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"6518","DOI":"10.1109\/TNNLS.2021.3082289","article-title":"Endmember-guided unmixing network (EGU-Net): A general deep learning framework for self-supervised hyperspectral unmixing","volume":"33","author":"Hong","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/817\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:20:21Z","timestamp":1760120421000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/817"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,31]]},"references-count":58,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030817"],"URL":"https:\/\/doi.org\/10.3390\/rs15030817","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,1,31]]}}}