Attention-Based Adaptive Spectral–Spatial Kernel ResNet for Hyperspectral Image Classification
Government of Himachal Pradesh · Chongqing University of Posts and Telecommunications · +1 more institution
Abstract
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 (A 2 S 2 K-ResNet) with spectral attention to capture…
Citation impact
- FWCI
- 40.35
- Percentile
- 100%
- References
- 96
Authors
4Topics & keywords
- Hyperspectral imaging
- Discriminative model
- Artificial intelligence
- Kernel (algebra)
- Pattern recognition (psychology)
- Computer science
- Convolutional neural network
- Spatial analysis
- Reduced inequalities