期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 59, 期 10, 页码 8754-8767出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3049377
关键词
Convolution; Feature extraction; Kernel; Standards; Computational modeling; Training; Neural networks; Attention mechanism; hyperspectral image (HSI) classification; lightweight structure; multilayer feature fusion; multiscale convolution
类别
资金
- State Key Program of National Natural Science of China [61836009]
- National Natural Science Foundation of China [61801353, 61876221]
- Fundamental Research Funds for the Central Universities [JB191907]
- Natural Science Basic Research Plan in Shaanxi Province of China [2019JQ-065]
- China Postdoctoral Science Foundation [2018M633474]
This article introduces a lightweight spectral-spatial attention feature fusion network guided by network architecture search, which achieves deeper network structure and higher efficiency in HSI classification.
Deep learning (DL) has become a hot topic in the research field of hyperspectral image (HSI) classification. However, with increasing depth and size of deep learning methods, its application in mobile and embedded vision applications has brought great challenges. In this article, we address a network architecture search (NAS)-guided lightweight spectral-spatial attention feature fusion network (LMAFN) for HSI classification. The overall architecture of the proposed network is guided by several conclusions of NAS, which achieves fewer parameters and lower computation cost with deeper network structure by exploiting multiscale Ghost grouped with efficient channel attention (ECA) module for adaptively adjusting the weights of different channels. It helps fully extract spectral-spatial discriminant features to avoid information loss of the dimension reduction operation. Specifically, a multilayer feature fusion method is proposed to extract the fusion information of the spectral-spatial features of each layer by considering complementary information of different hierarchical structures. Therefore, high-lever spectral-spatial attributes are gradually exploited along with the increase in layers and the fusion of layers. The experimental verification on three real HSI data sets demonstrates that the proposed framework presents more satisfying classification performance and efficiency with deeper network structure and lower parameter size.
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