The emergence of convolutional neural network (CNN) has greatly promoted the development of hyperspectral image (HSI) classification technology. However, the acquisition of HSI is difficult. Lack of training samples is the primary cause of low classification performance. Traditional CNN-based methods mainly use 2D CNN for feature extraction, which make interband correlations of HSIs underutilized. 3D CNN extracts the joint-spectral-spatial information representation, but it depends on a more complex model. Also, too deep or too shallow network cannot extract the image features well. To tackle these issues, we propose an HSI classification method based on 2D-3D CNN and multi-branch feature fusion. We first combine 2D CNN and 3D CNN to extract image features. Then, by means of the multi-branch neural network, three kinds of features from shallow to deep are extracted and fused in the spectral dimension. Finally, the fused features are passed into several fully connected layers and a softmax layer to obtain the classification results. In addition, our network model utilizes the state-of-the-art activation function Mish to further improve the classification performance. Our experimental results, conducted on four widely used HSI data sets, indicate that the proposed method achieves better performance than the existing alternatives.

Deep-learning methods, especially convolutional neural networks (CNN), have become the first choice for hyperspectral image (HSI) classification to date. It is a common procedure that small cubes are cropped from hyperspectral images and then fed into CNNs. However, standard CNNs find it difficult to extract discriminative spectral–spatial features. How to obtain finer spectral–spatial features to improve the classification performance is now a hot topic of research. In this regard, the attention mechanism, which has achieved excellent performance in other computer vision, holds the exciting prospect. In this paper, we propose a double-branch network consisting of a novel convolution named pyramidal convolution (PyConv) and an iterative attention mechanism. Each branch concentrates on exploiting spectral or spatial features with different PyConvs, supplemented by the attention module for refining the feature map. Experimental results demonstrate that our model can yield competitive performance compared to other state-of-the-art models.

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As the Hyperspectral (HS) images usually have low spatial resolution, hyperspectral image (HSI) super-resolution has recently attracted more and more attention to enhance the spatial resolution of HSIs. A common method is to fuse the low-resolution (LR) HSI with a multispectral image (MSI) whose spatial resolution is higher than the HSI. In this paper, we proposed a novel adaptive non-negative sparse representation (ANSR) based model to fuse an HSI and its corresponding MSI. First, basing the linear spectral unmixing, the non-negative structured sparse representation model estimates the sparse codes of the desired high-resolution (HR) HSI from both the LR-HSI and the MSI. Then, the adaptive sparse representation (ASR) can balance the relationship between the sparsity and collaboration by generating a suitable coefficient. Finally, in order to obtain more accurate results, we alternately optimize the spectral basis and coefficients rather than keeping the spectral basis fixed. Alternating direction method of multipliers is applied to solve the proposed optimization problem. Experimental results on both ground-based hyperspectral images and real remote sensing HSIs show the superiority of our proposed approach to some other state-of-the-art HSI super-resolution methods.