Rotation is All You Need: Cross Dimensional Residual Interaction for Hyperspectral Image Classification

The performance of deep convolutional neural networks has been significantly improved in recent years as a result of additional attention mechanisms applied to the standard networks. Numerous experiments conducted have demonstrated that spectral-spatial attention enhances the network's categorization ability. The three attention modules that currently use spatial attention, spectral attention, and channel attention are isolated from each other and their interrelationships are not fully considered. To solve this problem and establish the dependencies among different channels, spectral bands, spatial height, and width simultaneously, in this article, a new cross attention module called quadlet is proposed, which can capture information using simultaneous interaction of the channel, spectral depth and spatial location to improve the classification accuracy of hyperspectral images. By incorporating the quadlet attention module, a cross-dimensional residual network (QuadNet) is proposed for HSIs classification. A series of experiments conducted on four publicly available hyperspectral datasets showed that the proposed cross-attention residual network can effectively establish the dependencies among different dimensions of input tensor and achieve 98.22%, 99.88%, 99.10%, and 96.46% overall accuracy on IN, UP, SA, and UH datasets, respectively.