Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

Abstract

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

Improved Transformer Net for Hyperspectral Image Classification

“…We challenge the proposed SAT Net against convolutional neural network (CNN) [58] (CNN architecture with five layers of weights), spectral attention module-based convolutional network (SA-MCN) [40] (Recalibrate spatial information and spectral information), three-dimensional convolutional neural network (3D-CNN) [32], and the spectral-spatial residual network (SSRN) [25], and the multi-scale residual network model with an attention mechanism (MSRN) [41]. For fairness, we set the ratio of training set and test set to 2:8.…”

“…A good classification effect has been achieved on the HSI classification problem. These methods [36][37][38][39][40][41] achieved the best result in the SA dataset of 99.85% [37].…”

“…The models proposed in [33] and [35] converge at 50 and 100 epochs, respectively. To solve this problem, quite a few algorithms extract the spatial and spectral features separately and introduce the attention mechanism for HSI classification [36][37][38][39][40][41]. For example, Zhu et al [36] propose an end-to-end residual spectral-spatial attention network (RSSAN), which can adaptively realize the selection of spatial information and spectrum information.…”

“…Li et al [39] design a space spectrum attention network (JSSAN) to simultaneously capture the remote interdependence of spatial and spectral data through similarity assessment, and adaptively emphasize the characteristics of informational land cover and spectral bands, and Mou et al [40] improve the network by involving a network unit for the spectral attention module using the global spectrum space context and the learnable spectrum attention module to generate a series of spectrum gates reflecting the importance of the spectrum band. Qing et al [41] propose a multi-scale residual network model with an attention mechanism (MSRN). The model uses an improved residual network and spatial-spectral attention module to extract hyperspectral image information from different scales multiple times, fully integrates and extracts the spatial spectral features of the image.…”

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

“…A number of conventional spectral-based classifiers, such as support vector machines (SVM) [9,10], random forest [11][12][13], k-nearest neighbors (kNN) [14][15][16], Bayesian [17], etc., can only show good classification performance in the case of abundant labeled training samples. Recently, more and more methods based on deep learning have been applied to HSI classification tasks, and have achieved well results, for instance, generative adversarial networks (GAN) [18][19][20], recurrent neural networks (RNN) [21][22][23], fully convolutional network (FCN) [24][25][26] and convolution neural network (CNN) [27][28][29]. Among the above methods, the capability of CNN is peculiarly salient.…”