文献类型：期刊文献

英文题名：A Deep Stochastic Adaptive Fourier Decomposition Network for Hyperspectral Image Classification

作者：Cheng, Chunbo[1];Zhang, Liming[2];Li, Hong[3];Dai, Lei[4];Cui, Wenjing[1]

机构：[1]Hubei Polytechn Univ, Sch Math & Phys, Huangshi 435000, Hubei, Peoples R China;[2]Univ Macau, Fac Sci & Technol, Macau, Peoples R China;[3]Huazhong Univ Sci & Technol, Sch Math & Stat, Wuhan 430074, Hubei, Peoples R China;[4]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2024

卷号：33

起止页码：1080

外文期刊名：IEEE TRANSACTIONS ON IMAGE PROCESSING

收录：;EI(收录号：20240615527439);WOS:【SCI-EXPANDED(收录号:WOS:001350515600001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62206088 and Grant 61877021, in part by the Science and Technology Development Fund of Macau under Grant SAR 0041/2023/RIA1 and Grant 0060/2021/A, and in part by the Multi-Year Research Grant under Grant MYRG2022-00193-FST and Grant MYRG-GRG2023-00056-FST-UMDF. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Bart Goossens.

语种：英文

外文关键词：Stochastic adaptive Fourier decomposition; deep learning; CNN; HSIs classification

摘要：Deep learning-based hyperspectral image (HSI) classification methods have recently shown excellent performance, however, there are two shortcomings that need to be addressed. One is that deep network training requires a large number of labeled images, and the other is that deep network needs to learn a large number of parameters. They are also general problems of deep networks, especially in applications that require professional techniques to acquire and label images, such as HSI and medical images. In this paper, we propose a deep network architecture (SAFDNet) based on the stochastic adaptive Fourier decomposition (SAFD) theory. SAFD has powerful unsupervised feature extraction capabilities, so the entire deep network only requires a small number of annotated images to train the classifier. In addition, we use fewer convolution kernels in the entire deep network, which greatly reduces the number of deep network parameters. SAFD is a newly developed signal processing tool with solid mathematical foundation, which is used to construct the unsupervised deep feature extraction mechanism of SAFDNet. Experimental results on three popular HSI classification datasets show that our proposed SAFDNet outperforms other compared state-of-the-art deep learning methods in HSI classification.