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How to improve the spatial resolution as much as possible while maintaining the spectral information of multi-spectral (MS) image in the field of image fusion is of great significance for practical applications, such as map updating, feature classification, and target recognition. To analyze the coefficients of the subband distribution characteristics, in this paper, we propose a new panchromatic (PAN) and MS image pansharpening model based on an adaptive neural network and sparse representation in the non-subsample shearlet transform (NSST) domain. First, this algorithm is specific to regional directional characteristics in the high-frequency subband of PAN and MS images, and we propose an adaptive pulse coupled neural network (PCNN) model. The model can adaptively calculate the link strength of a neural cell based on the region energy. Furthermore, we apply the model to the high-frequency fusing process with the corresponding fusion rule, and the rule can distinguish the high-frequency coefficients by ignition times, which can more effectively capture the geometric texture information and detailed information in the PAN image, enhancing the spatial resolution of the fused image. Second, because of the low-frequency sub-bands from the PAN image and I component obtained by intensity-hue-saturation (IHS) transformation of the MS images with high similarity to the original image but poor sparsity, we select a set of PAN images for learning, a more targeted over-complete dictionary for low-frequency sub-band sparse representation is obtained. Then, the larger absolute value of the sparse matrix is selected to obtain the low-frequency coefficients for the fusion image while maintaining the MS spectral information effectively, and the representation of characteristic information of low-frequency subband is more effective. A large number of simulation experiments verify the effectiveness of the proposed method.

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Change detection methods using hyperspectral remote sensing can precisely identify differences of the same area at different observing times. However, due to massive spectral bands, current change detection methods are vulnerable to unrelatedspectral and spatial information in hyperspectral images with the stagewise calculation of attention maps. Besides, current change methods arrange hidden change features in a random distribution form, which cannot express a class-oriented discrimination in advance. Moreover, existent deep change methods have not fully considered the hierarchical features’ reuse and the fusion of the encoder–decoder framework. To better handle the mentioned existent problems, the parallel spectral–spatial attention network with feature redistribution loss (TFR−PS2ANet) is proposed. The contributions of this article are summarized as follows: (1) a parallel spectral–spatial attention module (PS2A) is introduced to enhance relevant information and suppress irrelevant information in parallel using spectral and spatial attention maps extracted from the original hyperspectral image patches; (2) the feature redistribution loss function (FRL) is introduced to construct the class-oriented feature distribution, which organizes the change features in advance and improves the discriminative abilities; (3) a two-branch encoder–decoder framework is developed to optimize the hierarchical transfer and change features’ fusion; Extensive experiments were carried out on several real datasets. The results show that the proposed PS2A can enhance significant information effectively and the FRL can optimize the class-oriented feature distribution. The proposed method outperforms most existent change detection methods.

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