{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bidirectional-convolutional-lstm-based","title":"Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification","arxiv_id":"1703.07910","date":"2017-03-23","proceeding":null,"authors":["Qingshan Liu","Feng Zhou","Renlong Hang","Xiao-Tong Yuan"],"abstract":"This paper proposes a novel deep learning framework named\nbidirectional-convolutional long short term memory (Bi-CLSTM) network to\nautomatically learn the spectral-spatial feature from hyperspectral images\n(HSIs). In the network, the issue of spectral feature extraction is considered\nas a sequence learning problem, and a recurrent connection operator across the\nspectral domain is used to address it. Meanwhile, inspired from the widely used\nconvolutional neural network (CNN), a convolution operator across the spatial\ndomain is incorporated into the network to extract the spatial feature.\nBesides, to sufficiently capture the spectral information, a bidirectional\nrecurrent connection is proposed. In the classification phase, the learned\nfeatures are concatenated into a vector and fed to a softmax classifier via a\nfully-connected operator. To validate the effectiveness of the proposed\nBi-CLSTM framework, we compare it with several state-of-the-art methods,\nincluding the CNN framework, on three widely used HSIs. The obtained results\nshow that Bi-CLSTM can improve the classification performance as compared to\nother methods.","url_abs":"http://arxiv.org/abs/1703.07910v1","url_pdf":"http://arxiv.org/pdf/1703.07910v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bidirectional-convolutional-lstm-based","repo_url":"https://github.com/atelili/2bivqa","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"hyperspectral-image-classification","task_name":"Hyperspectral Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}