乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:49:54Z","timestamp":1740149394438,"version":"3.37.3"},"reference-count":46,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,2,26]],"date-time":"2020-02-26T00:00:00Z","timestamp":1582675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Fundamental Research Funds for National University, China University of Geosciences (Wuhan)","award":["1910491T06"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61603355","61773355","61973285","61873249"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.<\/jats:p>","DOI":"10.3390\/s20051262","type":"journal-article","created":{"date-parts":[[2020,2,27]],"date-time":"2020-02-27T08:21:16Z","timestamp":1582791676000},"page":"1262","source":"Crossref","is-referenced-by-count":10,"title":["Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2803-884X","authenticated-orcid":false,"given":"Xiaoping","family":"Fang","sequence":"first","affiliation":[{"name":"The Department of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2609-3036","authenticated-orcid":false,"given":"Yaoming","family":"Cai","sequence":"additional","affiliation":[{"name":"The Department of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0020-6503","authenticated-orcid":false,"given":"Zhihua","family":"Cai","sequence":"additional","affiliation":[{"name":"The Department of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"The Beibu Gulf Big Data Resources Utilization Laboratory, Beibu Gulf University, Qinzhou 535011, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6783-2176","authenticated-orcid":false,"given":"Xinwei","family":"Jiang","sequence":"additional","affiliation":[{"name":"The Department of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zhikun","family":"Chen","sequence":"additional","affiliation":[{"name":"The Beibu Gulf Big Data Resources Utilization Laboratory, Beibu Gulf University, Qinzhou 535011, China"},{"name":"The Guangxi Key Laboratory of Marine Disaster in the Beibu Gulf, Beibu Gulf University, Qinzhou 535011, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1109\/TGRS.2013.2241773","article-title":"Nearest regularized subspace for hyperspectral classification","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, C., Ghamisi, P., Jia, X., and Gu, Y. (2017). Deep fusion of remote sensing data for accurate classification. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2017.2704625"},{"key":"ref_3","first-page":"441","article-title":"Hyperspectral data processing: Algorithm design and analysis. Photogramm","volume":"81","author":"Thenkabail","year":"2015","journal-title":"Eng. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5087","DOI":"10.1109\/JSTARS.2017.2737400","article-title":"Fast and robust self-representation method for hyperspectral band selection","volume":"10","author":"Sun","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","first-page":"13","article-title":"Introduction to hyperspectral image analysis","volume":"3","author":"Shippert","year":"2003","journal-title":"Online J. Space Commun."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cai, Y., Liu, X., and Cai, Z. (2019). BS-Nets: An End-to-End framework for band selection of hyperspectral image. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2019.2951433"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6808","DOI":"10.1109\/TGRS.2019.2908756","article-title":"Unsupervised spatial-spectral feature learning by 3D convolutional autoencoder for hyperspectral classification","volume":"57","author":"Mei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.1109\/TCYB.2018.2810806","article-title":"Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image","volume":"49","author":"Luo","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_9","first-page":"115","article-title":"Principal component analysis for hyperspectral image classification","volume":"62","author":"Rodarmel","year":"2002","journal-title":"Geo. Spat. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2693","DOI":"10.1109\/TGRS.2017.2651639","article-title":"Self-taught feature learning for hyperspectral image classification","volume":"55","author":"Kemker","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wu, J., Cai, Z., and Yu, P. (2020). Multi-view Multi-label Learning with Sparse Feature Selection for Image Annotation. IEEE Trans. Multimed., 1\u201314.","DOI":"10.1109\/TMM.2020.2966887"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2019.01.007","article-title":"An unsupervised parameter learning model for RVFL neural network","volume":"112","author":"Zhang","year":"2019","journal-title":"Neural Netw."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Agarwal, A., El-Ghazawi, T., El-Askary, H., and Le-Moigne, J. (2007, January 15\u201318). Efficient hierarchical-PCA dimension reduction for hyperspectral imagery. Proceedings of the 2007 IEEE International Symposium on Signal Processing and Information Technology, Giza, Egypt.","DOI":"10.1109\/ISSPIT.2007.4458191"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.13031\/2013.16565","article-title":"A novel integrated PCA and FLD method on hyperspectral image feature extraction for cucumber chilling damage inspection","volume":"47","author":"Cheng","year":"2004","journal-title":"Trans. ASABE"},{"key":"ref_16","first-page":"759","article-title":"Adaptation of an iterative PCA to a manycore architecture for hyperspectral image processing","volume":"91","author":"Lazcano","year":"2019","journal-title":"IET Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_18","unstructured":"Lin, Z.H., Chen, Y.S., Zhao, X., and Wang, G. (2013, January 10\u201313). Spectral-spatial classification of hyperspectral image using autoencoders. Proceedings of the 2013 9th International Conference on Information, Communications and Signal Processing (ICICS), Tainan, Taiwan."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Windrim, L., Ramakrishnan, R., Melkumyan, A., Murphy, R.J., and Chlingaryan, A. (2019). Unsupervised feature-learning for hyperspectral data with autoencoders. Remote Sens., 11.","DOI":"10.3390\/rs11070864"},{"key":"ref_20","unstructured":"Koda, S., Melgani, F., and Nishii, R. (2019). Unsupervised spectral-spatial feature extraction with generalized autoencoder for hyperspectral imagery. IEEE Geosci. Remote Sens. Lett., 1\u20135."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1109\/TIP.2016.2605010","article-title":"Graph regularized auto-encoders for image representation","volume":"26","author":"Liao","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liang, M., Jiao, L., and Meng, Z. (2019). A superpixel-based relational auto-encoder for feature extraction of hyperspectral images. Remote Sens., 11.","DOI":"10.3390\/rs11202454"},{"key":"ref_23","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Lasvegas, NV, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/LGRS.2015.2482520","article-title":"Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification","volume":"12","author":"Tao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","unstructured":"Huang, G.B., Zhu, Q.Y., and Siew, C.K. (2004, January 25\u201329). Extreme learning machine: a new learning scheme of feedforward neural networks. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Budapest, Hungary."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.patrec.2018.06.015","article-title":"Hierarchical ensemble of extreme learning machine","volume":"116","author":"Cai","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_27","first-page":"1","article-title":"Multi-View Fusion with Extreme Learning Machine for Clustering","volume":"10","author":"Zhang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.neucom.2013.09.070","article-title":"Ensemble of extreme learning machine for remote sensing image classification","volume":"149","author":"Han","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_29","first-page":"434","article-title":"Classification of hyperspectral remote sensing image using hierarchical local-receptive-field-based extreme learning machine","volume":"13","author":"Lv","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1109\/LGRS.2016.2568263","article-title":"Remote sensing image transfer classification based on weighted extreme learning machine","volume":"13","author":"Zhou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/JSTARS.2014.2359965","article-title":"Extreme learning machine with composite kernels for hyperspectral image classification","volume":"8","author":"Zhou","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Jiang, X., Wang, X., and Cai, Z. (2019). Spectral-Spatial Hyperspectral Image Classification with Superpixel Pattern and Extreme Learning Machine. Remote Sens., 11.","DOI":"10.3390\/rs11171983"},{"key":"ref_33","first-page":"31","article-title":"Representational learning with ELMs for big data","volume":"28","author":"Kasun","year":"2013","journal-title":"IEEE Intell. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","article-title":"Extreme learning machine for multilayer perceptron","volume":"27","author":"Tang","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9021","DOI":"10.1109\/ACCESS.2017.2706363","article-title":"Remote sensing image classification based on ensemble extreme learning machine with stacked autoencoder","volume":"5","author":"Lv","year":"2017","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ahmad, M., Khan, A.M., Mazzara, M., and Distefano, S. (2019, January 25\u201327). Multi-layer extreme learning machine-based autoencoder for hyperspectral image classification. Proceedings of the 14th International Conference on Computer Vision Theory and Applications (VISAPP\u201919), Prague, Czech Republic.","DOI":"10.5220\/0007258000750082"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3906","DOI":"10.1109\/TIP.2016.2570569","article-title":"Dimension reduction with extreme learning machine","volume":"25","author":"Kasun","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, P., Hastie, T.J., and Church, K.W. (2006, January 20\u201323). Very sparse random projections. Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA.","DOI":"10.1145\/1150402.1150436"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.1016\/j.jfranklin.2017.08.014","article-title":"Towards enhancing stacked extreme learning machine with sparse autoencoder by correntropy","volume":"355","author":"Luo","year":"2018","journal-title":"J. Franklin Inst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","article-title":"Trends in extreme learning machines: A review","volume":"61","author":"Huang","year":"2015","journal-title":"Neural Netw."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1007\/s12559-014-9255-2","article-title":"An insight into extreme learning machines: Random neurons, random features and kernels","volume":"6","author":"Huang","year":"2014","journal-title":"Cognit. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","article-title":"MOEA\/D: A multiobjective evolutionary algorithm based on decomposition","volume":"11","author":"Zhang","year":"2007","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TEVC.2004.826067","article-title":"Handling multiple objectives with particle swarm optimization","volume":"8","author":"Coello","year":"2004","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_45","unstructured":"Chollet, F. (2019, November 06). Keras. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4723","DOI":"10.1109\/TIT.2009.2027527","article-title":"Comparing measures of sparsity","volume":"55","author":"Hurley","year":"2009","journal-title":"IEEE Trans. Inf. Theory"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1262\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T02:38:44Z","timestamp":1736217524000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1262"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,26]]},"references-count":46,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20051262"],"URL":"https:\/\/doi.org\/10.3390\/s20051262","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,2,26]]}}}