With the rapid development of aerospace and various remote sensing platforms, the amount of data related to remote sensing is increasing rapidly. To meet the application requirements of remote sensing big data, an increasing number of scholars are combining deep learning with remote sensing data. In recent years, based on the rapid development of deep learning methods, research in the field of hyperspectral image (HSI) classification has seen continuous breakthroughs. In order to fully extract the characteristics of HSIs and improve the accuracy of image classification, this paper proposes a novel 3D channel and spatial attention-based multi-scale spatial'spectral residual network (termed CSMS-SSRN). The CSMS-SSRN framework uses a three-layer parallel residual network structure by using different 3D convolutional kernels to continuously learn spectral and spatial features from their respective residual blocks. Then, the extracted depth multi-scale features are stacked and input into the 3D attention module to enhance the expressiveness of the image features from the two aspects of channel and spatial domains, thereby improving the accuracy of classification. The CSMS-SSRN framework proposed in this paper can achieve better classification performance on different HSI datasets.

During the process of signal sampling and digital imaging, hyperspectral images (HSI) inevitably suffer from the contamination of mixed noises. The fidelity and efficiency of subsequent applications are considerably reduced along with this degradation. Recently, as a formidable implement for image processing, low-rank regularization has been widely extended to the restoration of HSI. Meanwhile, further exploration of the non-local self-similarity of low-rank images are proven useful in exploiting the spatial redundancy of HSI. Better preservation of spatial-spectral features is achieved under both low-rank and non-local regularizations. However, existing methods generally regularize the original space of HSI, the exploration of the intrinsic properties in subspace, which leads to better denoising performance, is relatively rare. To address these challenges, a joint method of subspace low-rank learning and non-local 4-d transform filtering, named SLRL4D, is put forward for HSI restoration. Technically, the original HSI is projected into a low-dimensional subspace. Then, both spectral and spatial correlations are explored simultaneously by imposing low-rank learning and non-local 4-d transform filtering on the subspace. The alternating direction method of multipliers-based algorithm is designed to solve the formulated convex signal-noise isolation problem. Finally, experiments on multiple datasets are conducted to illustrate the accuracy and efficiency of SLRL4D.

Hyperspectral image (HSI) super-resolution (SR) is an important technique for improving the spatial resolution of HSI. Recently, a method based on sparse representation improved the performance of HSI SR significantly. However, the spectral dictionary was learned under a fixed size, empirically, without considering the training data. Moreover, most of the existing methods fail to explore the relationship among the sparse coefficients. To address these crucial issues, an effective method for HSI SR is proposed in this paper. First, a spectral dictionary is learned, which can adaptively estimate a suitable size according to the input HSI without any prior information. Then, the proposed method exploits the nonlocal correlation of the sparse coefficients. Double ${\ell }_1$ regularized sparse representation is then introduced to achieve better reconstructions for HSI SR. Finally, a high spatial resolution HSI is generated by the obtained coefficients matrix and the learned adaptive size spectral dictionary. To evaluate the performance of the proposed method, we conduct experiments on two famous datasets. The experimental results demonstrate that it can outperform some relatively state-of-the-art methods in terms of the popular universal quality evaluation indexes.

Pansharpening is an effective way to enhance the spatial resolution of a multispectral (MS) image by fusing it with a provided panchromatic image. Instead of restricting the coding coefficients of low-resolution (LR) and high-resolution (HR) images to be equal, we propose a pansharpening approach via sparse regression in which the relationship between sparse coefficients of HR and LR MS images is modeled by ridge regression and elastic-net regression simultaneously learning the corresponding dictionaries. The compact dictionaries are learned based on the sampled patch pairs from the high- and low-resolution images, which can greatly characterize the structural information of the LR MS and HR MS images. Later, taking the complex relationship between the coding coefficients of LR MS and HR MS images into account, the ridge regression is used to characterize the relationship of intrapatches. The elastic-net regression is employed to describe the relationship of interpatches. Thus, the HR MS image can be almost identically reconstructed by multiplying the HR dictionary and the calculated sparse coefficient vector with the learned regression relationship. The simulated and real experimental results illustrate that the proposed method outperforms several well-known methods, both quantitatively and perceptually.

Most existing superresolution (SR) techniques focus primarily on improving the quality in the luminance component of SR images, while paying less attention to the chrominance component. We present an edge and color preserving image SR approach. First, for the luminance channel, a heavy-tailed gradient distribution of natural images is investigated as an image prior. Then, an efficient optimization algorithm is developed to recover the latent high-resolution (HR) luminance component. Second, for the chrominance channels, we propose a two-stage framework for luminance-guided chrominance SR. In the first stage, since most of the shape and structural information is contained in the luminance channel, a simple Markov random field formulation is introduced to search the optimal direction for color local interpolation guided by HR luminance components. To further improve the quality of the chrominance channels, in the second stage, a nonlocal auto regression model is utilized to refine the initial HR chrominance. Finally, we combine the SR reconstructed luminance components with the generated HR chrominance maps to get the final SR color image. Systematic experimental results demonstrated that our method outperforms some state-of-the-art methods in terms of the peak signal-to-noise ratio, structural similarity, feature similarity, and the mean color errors.

We proposed a superresolution (SR) method based on example-learning framework. In our framework, the relationship between the output high-resolution (HR) estimation and the HR training images is approximated by the relationship between the low-resolution (LR) test image and the HR training images. To effectively capture the strong correlation between LR and HR images, the LR and HR images are mapped onto a common feature space. Furthermore, in order to maintain their original two-dimensional (2-D) spatial structure, the original LR and HR patches are mapped onto the underlying common feature space using 2-D canonical correlation analysis. Later, the relationship between HR and LR features is established by partial least squares (PLS) with low regression errors on the derived feature space. In addition, a steering kernel regression (SKR) constraint is integrated into patch aggregation to improve the quality of the recovered images. Finally, the effectiveness of our approach is validated by extensive experimental comparisons with several SR algorithms for the natural image superresolution both quantitatively and qualitatively.