{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T12:14:58Z","timestamp":1778847298020,"version":"3.51.4"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2018,5]]},"abstract":"<jats:p> In this paper, we propose a novel scheme to learn high-level representative features and conduct classification for hyperspectral image (HSI) data in an automatic fashion. The proposed method is a collaboration of a wavelet-based extended morphological profile (WTEMP) and a deep autoencoder (DAE) (\u201cWTEMP-DAE\u201d), with the aim of exploiting the discriminative capability of DAE when using WTEMP features as the input. Each part of WTEMP-DAE is ingenious and contributes to the final classification performance. Specifically, in WTEMP-DAE, the spatial information is extracted from the WTEMP, which is then joined with the wavelet denoised spectral information to form the spectral-spatial description of HSI data. The obtained features are fed into DAE as the original input, where the good weights and bias of the network are initialized through unsupervised pre-training. Once the pre-training is completed, the reconstruction layers are discarded and a logistic regression (LR) layer is added to the top of the network to perform supervised fine-tuning and classification. Experimental results on two real HSI data sets demonstrate that the proposed strategy improves classification performance in comparison with other state-of-the-art hand-crafted feature extractors and their combinations. <\/jats:p>","DOI":"10.1142\/s0219691318500169","type":"journal-article","created":{"date-parts":[[2017,11,28]],"date-time":"2017-11-28T04:16:23Z","timestamp":1511842583000},"page":"1850016","source":"Crossref","is-referenced-by-count":12,"title":["Wavelet-based extended morphological profile and deep autoencoder for hyperspectral image classification"],"prefix":"10.1142","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5908-5518","authenticated-orcid":false,"given":"Huiwu","family":"Luo","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, University of Macau, Macau 999078, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan Yan","family":"Tang","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, University of Macau, Macau 999078, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert P.","family":"Biuk-Aghai","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, University of Macau, Macau 999078, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, University of Macau, Macau 999078, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer, Electronics and Information, Guangxi University, Nanning 530004, P. R. 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