Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
Hyperspectral images (HSIs) provide rich spectral-spatial information with stacked hundreds of contiguous narrowbands. Due to the existence of noise and band correlation, the selection of informative spectral-spatial kernel features poses a challenge. This is often addressed by using convolutional neural networks (CNNs) with receptive field (RF) having fixed sizes. However, these solutions cannot enable neurons to effectively adjust RF sizes and cross-channel dependencies when forward and backward propagations are used to optimize the network. In this article, we present an attention-based adaptive spectral-spatial kernel improved residual network (A²S²K-ResNet) with spectral attention to capture discriminative spectral-spatial features for HSI classification in an end-to-end training fashion. In particular, the proposed network learns selective 3-D convolutional kernels to jointly extract spectral-spatial features using improved 3-D ResBlocks and adopts an efficient feature recalibration (EFR) mechanism to boost the classification performance. Extensive experiments are performed on three well-known hyperspectral data sets, i.e., IP, KSC, and UP, and the proposed A²S²K-ResNet can provide better classification results in terms of overall accuracy (OA), average accuracy (AA), and Kappa compared with the existing methods investigated. The source code will be made available at https://github.com/suvojit-0x55aa/A2S2K-ResNet.
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Results from the Paper
Ranked #1 on Hyperspectral Image Classification on Kennedy Space Center (Overall Accuracy metric)
Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Hyperspectral Image Classification | Indian Pines | A2S2K-ResNet | Overall Accuracy | 99.57 % | # 7 | |
Hyperspectral Image Classification | Kennedy Space Center | A2S2K-ResNet | Overall Accuracy | 99.34 | # 1 | |
Hyperspectral Image Classification | Pavia University | A2S2K-ResNet | Overall Accuracy | 99.85 | # 6 |