Hyperspectral Image Classification Based on Multiscale Hybrid Networks and Attention Mechanisms

Abstract

Hyperspectral image (HSI) classification is one of the most crucial tasks in remote sensing processing. The attention mechanism is preferable to a convolutional neural network (CNN), due to its superior ability to express information during HSI processing. Recently, numerous methods combining CNNs and attention mechanisms have been applied in HSI classification. However, it remains a challenge to achieve high-accuracy classification by fully extracting effective features from HSIs under the conditions of limited labeled samples. In this paper, we design a novel HSI classification network based on multiscale hybrid networks and attention mechanisms. The network consists of three subnetworks: a spectral-spatial feature extraction network, a spatial inverted pyramid network, and a classification network, which are employed to extract spectral-spatial features, to extract spatial features, and to obtain classification results, respectively. The multiscale fusion network and attention mechanisms complement each other by capturing local and global features separately. In the spatial pyramid network, multiscale spaces are formed through down-sampling, which can reduce redundant information while retaining important information. The structure helps the network better capture spatial features at different scales, and to improve classification accuracy. Experimental results on various public HSI datasets demonstrate that the designed network is extremely competitive compared to current advanced approaches, under the condition of insufficient samples.

ResMorCNN Model: Hyperspectral Images Classification Using Residual-Injection Morphological Features and 3DCNN Layers

“…3DCNN [68], MDCNN [44], hybrid spectral networks (HybridSN) [28], morphological CNNs for HSI classification (MCHNet) [46], A2S2K-ResNet [69], asymmetric inception network (AINeT), which utilizes a 3-D asymmetric inception network with data fusion transfer learning [70], HSSAM, which employs a heterogeneous spectralspatial network with 3-D attention and MLP while dealing with limited training samples [38], MHNA, which is based on multiscale hybrid networks and attention mechanisms [71], and MorphFormer [49]. Traditional machine-learning techniques for classification include DT, RF, and SVM.…”

A Dual-Attention Deep Discriminative Domain Generalization Model for Hyperspectral Image Classification

“…Shi et al [39] proposed the 3D-OCONV method and abstracted the spectral features by combining 3D-OCONV and spectral attention. Pan et al [40] applied an attention mechanism to extract global features and designed a multi-scale fusion module to extract local features, which effectively captured spatial features at different scales.…”