In recent years, deep learning-based models have produced encouraging results for hyperspectral image (HSI) classification. Specifically, Convolutional Long Short-Term Memory (ConvLSTM) has shown good performance for learning valuable features and modeling long-term dependencies in spectral data. However, it is less effective for learning spatial features, which is an integral part of hyperspectral images. Alternatively, convolutional neural networks (CNNs) can learn spatial features, but they possess limitations in handling long-term dependencies due to the local feature extraction in these networks. Considering these factors, this paper proposes an end-to-end Spectral-Spatial 3D ConvLSTM-CNN based Residual Network (SSCRN), which combines 3D ConvLSTM and 3D CNN for handling both spectral and spatial information, respectively. The contribution of the proposed network is twofold. Firstly, it addresses the long-term dependencies of spectral dimension using 3D ConvLSTM to capture the information related to various ground materials effectively. Secondly, it learns the discriminative spatial features using 3D CNN by employing the concept of the residual blocks to accelerate the training process and alleviate the overfitting. In addition, SSCRN uses batch normalization and dropout to regularize the network for smooth learning. The proposed framework is evaluated on three benchmark datasets widely used by the research community. The results confirm that SSCRN outperforms state-of-the-art methods with an overall accuracy of 99.17%, 99.67%, and 99.31% over Indian Pines, Salinas, and Pavia University datasets, respectively. Moreover, it is worth mentioning that these excellent results were achieved with comparatively fewer epochs, which also confirms the fast learning capabilities of the SSCRN.
Hyperspectral unmixing (HSU) is a crucial method to determine the fractional abundance of the material (endmembers) in each pixel. Most spectral unmixing methods are affected by low signal-to-noise ratios because of noisy pixels and bands simultaneously, requiring robust HSU techniques that exploit both 3D (spectral–spatial dimension) and 2D (spatial dimension) domains. In this paper, we present a new method for robust supervised HSU based on a deep hybrid (3D and 2D) convolutional autoencoder (DHCAE) network. Most HSU methods adopt the 2D model for simplicity, whereas the performance of HSU depends on spectral and spatial information. The DHCAE network exploits spectral and spatial information of the remote sensing images for abundance map estimation. In addition, DHCAE uses dropout to regularize the network for smooth learning and to avoid overfitting. Quantitative and qualitative results confirm that our proposed DHCAE network achieved better hyperspectral unmixing performance on synthetic and three real hyperspectral images, i.e., Jasper Ridge, urban and Washington DC Mall datasets.
Graph convolutional networks (GCN) can extract features from non-Euclidean space very effectively, and it has been successfully applied in various fields of hyperspectral images (HSIs). However, due to the limited labeled HSI data, GCN often performs not well and encounters over-smoothing problems as the number of network layers increases. Furthermore, building a GCN adjacency matrix for HSI classification directly is computationally complex. This paper proposes a multiscale semantic alignment graph convolutional network (MSAGCN) for HSI classification to solve the problems mentioned above. The proposed method mainly consists of three parts, superpixel segmentation, semantic alignment and multiscale graphs. Firstly, superpixel segmentation is performed on the original HSI, and each superpixel region contains similar spatial and spectral information. Secondly, semantic features of labeled nodes are extracted using identity aggregation with fixed receptive fields. The class-center similarity is adopted using these semantic features to align nodes semantically. This semantic alignment technique alleviates over-smoothing in GCN. Finally, the multiscale technique enables MSAGCN to obtain different scale spectral-spatial features. Experimental results show that our proposed model exhibits competitive results on open source hyperspectral datasets with only one labeled sample per class.
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