Search

Recently, with the remarkable advancements of deep learning in the field of image processing, convolutional neural networks (CNNs) have garnered widespread attention from researchers in the domain of hyperspectral image (HSI) classification. Moreover, due to the high performance demonstrated by the transformer architecture in classification tasks, there has been a proliferation of neural networks combining CNNs and transformers for HSI classification. However, the majority of the current methods focus on extracting spatial–spectral features from the HSI data of a single size for a pixel, overlooking the rich multi-scale feature information inherent to the data. To address this problem, we designed a novel transformer network with a CNN-enhanced cross-attention (TNCCA) mechanism for HSI classification. It is a dual-branch network that utilizes different scales of HSI input data to extract shallow spatial–spectral features using a multi-scale 3D and 2D hybrid convolutional neural network. After converting the feature maps into tokens, a series of 2D convolutions and dilated convolutions are employed to generate two sets of Q (queries), K (keys), and V (values) at different scales in a cross-attention module. This transformer with CNN-enhanced cross-attention explores multi-scale CNN-enhanced features and fuses them from both branches. Experimental evaluations conducted on three widely used hyperspectral image (HSI) datasets, under the constraint of limited sample size, demonstrate excellent classification performance of the proposed network. [ABSTRACT FROM AUTHOR]

In the past decade, deep learning methods have proven to be highly effective in the classification of hyperspectral images (HSI), consistently outperforming traditional approaches. However, the large number of spectral bands in HSI data can lead to interference during the learning process. To address this issue, dimensionality reduction techniques can be employed to minimize data redundancy and improve HSI classification performance. Hence, we have developed an efficient lightweight learning framework consisting of two main components. Firstly, we utilized band selection and principal component analysis to reduce the dimensionality of HSI data, thereby reducing redundancy while retaining essential features. Subsequently, the pre-processed data was input into a modified VGG-based learning network for HSI classification. This method incorporates an improved dynamic activation function for the multi-layer perceptron to enhance non-linearity, and reduces the number of nodes in the fully connected layers of the original VGG architecture to improve speed while maintaining accuracy. This modified network structure, referred to as lightweight-VGG (LVGG), was specifically designed for HSI classification. Comprehensive experiments conducted on three publicly available HSI datasets consistently demonstrated that the LVGG method exhibited similar or better performance compared to other typical methods in the field of HSI classification. Our approach not only addresses the challenge of interference in deep learning methods for HSI classification, but also offers a lightweight and efficient solution for achieving high classification accuracy. [ABSTRACT FROM AUTHOR]