期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 59, 期 1, 页码 449-462出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.2994057
关键词
Feature extraction; Hyperspectral imaging; Task analysis; Training; Adaptation models; Machine learning; Attention network; convolutional neural networks (CNNs); hyperspectral image(HSI) classification; residual spectral-spatial attention network (RSSAN); spatial attention; spectral attention
类别
资金
- State Key Program of National Natural Science of China [61836009]
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61621005]
- National Natural Science Foundation of China [U1701267, 61871310, 61573267, 61801353]
- Fund for Foreign Scholars in University Research and Teaching Programs (111 Project) [B07048]
- Major Research Plan of the National Natural Science Foundation of China [91438201, 91438103]
- Program for Cheung Kong Scholars and Innovative Research Team in University [IRT_15R53]
- Natural Science Basic Research Plan In Shaanxi Province of China [2019JQ-065]
- China Postdoctoral Science Foundation [2018M633474]
- Fundamental Research Funds for the Central Universities [JB191907]
This article proposes an end-to-end residual spectral-spatial attention network (RSSAN) for hyperspectral image classification, utilizing spectral and spatial attention mechanisms to extract effective features for classification and adaptive feature refinement, ultimately achieving superior classification accuracy on three HSI datasets.
In the last five years, deep learning has been introduced to tackle the hyperspectral image (HSI) classification and demonstrated good performance. In particular, the convolutional neural network (CNN)-based methods for HSI classification have made great progress. However, due to the high dimensionality of HSI and equal treatment of all bands, the performance of these methods is hampered by learning features from useless bands for classification. Moreover, for patchwise-based CNN models, equal treatment of spatial information from the pixel-centered neighborhood also hinders the performance of these methods. In this article, we propose an end-to-end residual spectral-spatial attention network (RSSAN) for HSI classification. The RSSAN takes raw 3-D cubes as input data without additional feature engineering. First, a spectral attention module is designed for spectral band selection from raw input data by emphasizing useful bands for classification and suppressing useless bands. Then, a spatial attention module is designed for the adaptive selection of spatial information by emphasizing pixels from the same class as the center pixel or those are useful for classification in the pixel-centered neighborhood and suppressing those from a different class or useless. Second, two attention modules are also used in the following CNN for adaptive feature refinement in spectral-spatial feature learning. Third, a sequential spectral-spatial attention module is embedded into a residual block to avoid overfitting and accelerate the training of the proposed model. Experimental studies demonstrate that the RSSAN achieved superior classification accuracy compared with the state of the art on three HSI data sets: Indian Pines (IN), University of Pavia (UP), and Kennedy Space Center (KSC).
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