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المؤلفAhishali, Mete
المؤلفKiranyaz, Serkan
المؤلفAhmad, Iftikhar
المؤلفGabbouj, Moncef
تاريخ الإتاحة2023-09-24T08:57:19Z
تاريخ النشر2022
اسم المنشورProceedings - International Conference on Image Processing, ICIP
المصدرScopus
الرقم المعياري الدولي للكتاب2381-8549
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/ICIP46576.2022.9897863
معرّف المصادر الموحدhttp://hdl.handle.net/10576/47893
الملخصThe band selection in the hyperspectral image (HSI) data processing is an important task considering its effect on the computational complexity and accuracy. In this work, we propose a novel framework for the band selection problem: Self-Representation Learning (SRL) with Sparse 1D-Operational Autoencoder (SOA). The proposed SLR-SOA approach introduces a novel autoencoder model, SOA, that is designed to learn a representation domain where the data are sparsely represented. Moreover, the network composes of 1D-operational layers with the non-linear neuron model. Hence, the learning capability of neurons (filters) is greatly improved with shallow architectures. Using compact architectures is especially crucial in autoencoders as they tend to overfit easily because of their identity mapping objective. Overall, we show that the proposed SRL-SOA band selection approach outperforms the competing methods over two HSI data including Indian Pines and Salinas-A considering the achieved land cover classification accuracies. The software implementation of the SRL-SOA approach is shared publicly.
اللغةen
الناشرIEEE Computer Society
الموضوعBand selection
hyperspectral image data
machine learning
self-representation learning
sparse autoencoders
العنوانSRL-SOA: SELF-REPRESENTATION LEARNING WITH SPARSE 1D-OPERATIONAL AUTOENCODER FOR HYPERSPECTRAL IMAGE BAND SELECTION
النوعConference Paper
الصفحات2296-2300
dc.accessType Abstract Only


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