Attention-Based Adaptive Spectral–Spatial Kernel ResNet for Hyperspectral Image Classification

By Swalpa Kumar Roy, Suvojit Manna, Tiecheng Song, Lorenzo Bruzzone. IEEE Transactions on Geoscience and Remote Sensing, 2020

444 citations recorded by the source corpus.

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 (<monospace>A²S²K-ResNet</monospace>) 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 <monospace>A²S²K-ResNet</monospace> 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 <uri>https://github.com/suvojit-</uri><inline-formula> <tex-math notation="LaTeX">$0\times 55$ </tex-math></inline-formula>aa/A2S2K-ResNet.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/e1fd01c83e0d5389dfc9a297b768ced4e0cf3a86

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Antonio J. Plaza
2. Danfeng Hong
3. Qian Du
4. Swalpa Kumar Roy
5. Juan Mario Haut
6. Jocelyn Chanussot
7. Jing Yao
8. Chiranjibi Shah

In Media Technology

1. Antonio J. Plaza
2. Jocelyn Chanussot
3. Danfeng Hong
4. Swalpa Kumar Roy
5. Jing Yao
6. Juan Mario Haut
7. Qian Du
8. Xin Wu

Citations in the corpus

• Multimodal Fusion Transformer for Remote Sensing Image Classification (2022, methodology): “Conventional methods have been widely used for HSI classification, even with limited training samples [20]–[22].”
• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, background): “and led to improved classification accuracy [67], [150]–[154].”
• Spectral–Spatial Morphological Attention Transformer for Hyperspectral Image Classification (2023, background): “To take full advantage of ResNets, they can be extended to form even more complex models, such as the inclusion of adaptive kernels [17], lightweight spatial–spectral attention based on squeeze-and-excitation [35], and pyramidal ResNets [37].”