Hyperspectral image (HSI) classification is a vital but difficult task due to its significant spectral variability and nonlinear structure. Nowadays, complex spatial-spectral networks have achieved remarkable successes in HSI classification, but limited by the large complexity and hardware demands. Spectral networks with simple architectures alleviate this problem to some degree, however, most of them have downgraded performance as a result of insufficient excavation of spectral diagonal information and channel correlations. To overcome these problems, this paper proposes a fresh atrous convolution channel attention aided dense convolutional neural network (ACADCN) for HSI classification, which enhances the exploitation of spectral feature representations and channel correlations to provide a better classification with limited samples. On the one hand, an effective 1D dense block is constructed to deeply mine spectral discriminability by taking advantages of hierarchical representations and establish a deep 1D convolutional neural network, with the complementarity of different level features integrated. On the other hand, a singularly designed atrous convolution channel attention (ACA) module is used to learn multiscale cross-channel correlations to make up the locality of convolutions. The effectiveness of ACADCN is verified on two commonly used HSIs, with a mean overall accuracy (OA) of 94.09%, average accuracy (AA) of 94.63% and Kappa of 0.9254 achieved. The experimental results show its superiority to the other advanced deep spectral classifiers.

Cloud detection is a prerequisite for optical remote sensing applications due to the ubiquitous cloud coverage and negative effect of cloud occlusions. However, it is very challenging because of the heterogeneity of clouds and diversity of underlying surfaces, especially for thin clouds with illegible shapes and dispersed distribution in addition to high transparency, which is a common bottleneck for most methods. To tackle these problems, this paper proposes an innovative attention aided multilevel cloud detection network (AMCD-Net) for optical satellite imagery. Specifically, AMCD-Net takes full account into the variability and complexity of clouds and integrates multilevel features and different attentions within a deep convolutional U-Net framework, thus enabling more accurate cloud identification in complex scenarios. On the one hand, a multilevel asymmetric convolutional module (MAC) embedded encoder is established to learn discriminative representations for clouds with various shapes and integrate the complementarity of multilevel features to improve model robustness, with a regional attention-based decoder constructed to more accurately recover complicated cloud distribution, which effectively balances the integrity and details of clouds. On the other hand, a deformable convolution-based geometry enhancement attention (GEA) is designed to refine information transmission between the encoder and decoder, with a joint loss of binary cross-entropy (BCE) and structural similarity index (SSIM) constructed to simultaneously focus on category and morphology discriminant excavation, which are favorable for fine-grained cloud prediction. The effectiveness of AMCD-Net was verified on two well-known datasets, i.e., 38-Cloud dataset and SPARCS dataset, and the results demonstrate that it outperformed the other state-of-the-art deep networks.

Scene classification of high-spatial resolution imagery (HSRI) includes various potential applications in various fields. Recently, deep convolutional neural networks (CNNs) have achieved competitive performance as a result of the powerful capability of feature extraction. In this paper, we propose a multi-level improved circle pooling (MICP) method with the pre-trained CNN-based model to enhance the discriminative power of CNN activations for scene classification. Specifically, an improved pooling strategy is presented to generate annular subregions without padding operations in traditional concentric circle pooling. Then, we extract the pooling features in these subregions under different levels and build a holistic representation by fusing these multi-level features. MICP is an effective and simple strategy enriching rotation insensitivity and multiscale spatial information. According to the experiments conducted on three challenging HRSI scene data sets, the proposed pooling method achieves similar or better classification accuracy compared to the other CNN-based scene classification methods. Moreover, a comprehensive discussion regarding the effect of data augmentation reveals that the proposed method can enhance the rotation insensitivity of CNNs for the HRSI scene classification.

Clustering is a common method for hyperspectral image (HSI) interpretation in the case of no labeled samples. Many subspace clustering methods have now been proposed for HSIs and have obtained remarkable success. However, because of the prohibitively large computational complexity induced by the self-dictionary representation, these methods suffer from the scalability issue and are ineffective for large HSIs. In this article, to address this issue, we focus on a scalable subspace clustering scheme and introduce the recently developed sketched subspace clustering (sketched-SC) model to HSI. The sketched-SC model is computationally inexpensive and is suitable for the large HSI clustering task as it constructs a compact yet expressive dictionary. However, several problems degrade the performance of sketched-SC, i.e., the inadequate mining of the structural information and no consideration of spatial information. In view of this, a novel scalable nonlocal means regularized sketched reweighted sparse and low-rank (NL-SSLR) SC algorithm is proposed for use with large HSIs. On the one hand, the SSLR representation model is constructed to explore the underlying local and global structural information of the HSIs at the same time. On the other hand, the nonlocal means regularization is used to fully explore the spatial correlation information and better account for the self-similarity of HSIs, to further boost the clustering performance. The experimental results obtained on two well-known hyperspectral data sets corroborate the superiority of the proposed algorithm over the other state-of-the-art HSI clustering methods.

Hyperspectral image (HSI) clustering is extremely challenging because of the complexity of the image structure. Recently, the subspace clustering algorithms have achieved competitive performance for HSIs. However, these methods generally are computationally complex and time-and-memory-consuming, given their reliance on large-scale adjacency matrix learning and graph segmentation, which limits their application to large HSIs and reduces their attractiveness in real applications. In this article, in view of this, two novel sparsity-based clustering algorithms are proposed for large HSIs, named sparse coding-based clustering (SCC) and joint SCC (JSCC). To the best of our knowledge, we are the first to use the sparse representation recovery residual to cluster HSIs. Based on a structured dictionary constructed by$k$-means and$k$-nearest neighbor (KNN), an SCC model is constructed to cluster HSIs according to the recovery residual minimization criterion. By dealing with a pixel-wise sparse recovery problem instead of the large-scale graph optimization problem of the whole image, the computational complexity and the time-and-memory cost are reduced to a large degree, which makes sense for practical applications. Then, by introducing the super-pixel neighborhood, a JSCC model is constructed to better explore the interpixel correlation of HSIs and further improve the clustering performance. The proposed algorithms were verified on three widely used HSIs. All the three experiments confirm the effectiveness of the proposed algorithms, which can be considered as competitive tools for use with large HSIs.

Clustering is a very challenging task for hyperspectral imagery (HSI) because of the complex spectral-spatial structures found in such data. Recently, the sparse recovery-based approaches have been introduced to deal with hyperspectral clustering, and have achieved state-of-the-art performances. Several recent works have shown that it is the collaborative representation mechanism over all the dictionary atoms, rather than the sparse constraint that determines the recognition performance. Based on this fact, in this paper, we focus on the working mechanism of collaborative representation to explore its potential in HSI clustering. However, directly introducing collaborative representation clustering (CRC) to HSIs results in several problems, i.e., the high redundancy of the global dictionary atoms and the absence of spatial information, which greatly limit the clustering performance. In view of this, we propose a novel total variation regularized CRC with a locally adaptive dictionary (TV-CRC-LAD) algorithm for HSI. First, the LAD construction strategy is introduced instead of the global dictionary to relieve the high redundancy and the interference of unrelated atoms in the representation process, to more precisely represent each pixel only with the highly correlated atoms. Second, TV regularization is integrated to better account for the rich spatial-contextual information and promotes the piecewise smoothness of the HSI clustering result. The proposed algorithm was tested on three widely used hyperspectral data sets, and the experimental results clearly illustrate that the proposed algorithm outperforms the corresponding sparsity-based clustering methods and the other state-of-the-art methods.

Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. Recently, sparse representation (SR) theory has been successfully introduced to HSI band selection, and many SR-based methods have been developed and shown great potential and superiority. However, due to the inherent limitations of the SR scheme, i.e., individually representing each band with only a few other bands from the same subspace, the SR-based methods cannot effectively capture the global structures of the data, which limit the band selection performance. In this paper, to overcome this obstacle, the novel Laplacian-regularized low-rank subspace clustering (LLRSC) algorithm is proposed for HSI band selection. On the one hand, the low-rank subspace clustering model is introduced to capture the global structure information for the learned representation coefficient matrix and deal with the HSI band selection task in the clustering framework. On the other hand, considering the high correlation between adjacent bands, 1-D Laplacian regularization is utilized to incorporate the neighboring band information and further reduce the representation bias. Lastly, an eigenvalue analysis algorithm based on band mutation information is utilized to estimate the appropriate size of the band subset. The experimental results indicate that the proposed LLRSC algorithm outperforms the other state-of-the-art methods and achieves a very competitive band selection performance for HSIs.

In this paper, we propose total variation regularized collaborative representation clustering with a locally adaptive dictionary for hyperspectral remote sensing imagery. With regard to the high redundancy of the global dictionary and the interference of unrelated dictionary atoms in the representation process, the collaborative representation clustering model with a locally adaptive dictionary is introduced to more precisely represent each pixel only with highly correlated atoms. In addition, total variation regularization is integrated to better account for the rich spatial contextual information. The extensive experimental results clearly illustrate the superiority of the proposed algorithm.

Robust techniques such as sparse subspace clustering (SSC) have been recently developed for hyperspectral images (HSIs) based on the assumption that pixels belonging to the same land-cover class approximately lie in the same subspace. In order to account for the spatial information contained in HSIs, SSC models incorporating spatial information have become very popular. However, such models are often based on a local averaging constraint, which does not allow for a detailed exploration of the spatial information, thus limiting their discriminative capability and preventing the spatial homogeneity of the clustering results. To address these relevant issues, in this letter, we develop a new and effective ℓ2-norm regularized SSC algorithm which adds a four-neighborhood ℓ2-norm regularizer into the classical SSC model, thus taking full advantage of the spatial-spectral information contained in HSIs. The experimental results confirm the potential of including the spatial information (through the newly added ℓ2-norm regularization term) in the SSC framework, which leads to a significant improvement in the clustering accuracy of SSC when applied to HSIs.

Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. In this paper, a novel squaring weighted low-rank subspace clustering band selection (SWLRSC) algorithm is proposed for hyperspectral imagery. The SWLRSC method can effectively capture the global structure information of the HSI band set by constructing a strongly connected adjacency matrix with accurate representation coefficients, and can adaptively determine an appropriate size for the selected band subset. The experimental results indicate that the proposed SWLRSC algorithm outperforms the state-of-the-art band selection algorithms.

Clustering for hyperspectral images (HSIs) is a very challenging task due to its inherent complexity. In this paper, we propose a novel spectral-spatial sparse subspace clustering S4C algorithm for hyperspectral remote sensing images. First, by treating each kind of land-cover class as a subspace, we introduce the sparse subspace clustering (SSC) algorithm to HSIs. Then, considering the spectral and spatial properties of HSIs, the high spectral correlation and rich spatial information of the HSIs are taken into consideration in the SSC model to obtain a more accurate coefficient matrix, which is used to build the adjacent matrix. Finally, spectral clustering is applied to the adjacent matrix to obtain the final clustering result. Several experiments were conducted to illustrate the performance of the proposed S4C algorithm.