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Hyperspectral images are usually acquired in a scanning-based way, which can cause inconvenience in some situations. In these cases, RGB image spectral super-resolution technology emerges as an alternative. However, current mainstream spectral super-resolution methods aim to generate continuous spectral information at a very narrow range, limited to the visible light range. Some researchers introduce hyperspectral images as auxiliary data. But it is usually required that the auxiliary hyperspectral images have the same spatial range as RGB images. To address this issue, a general point–surface data fusion method is designed to achieve the RGB image spectral super-resolution goal in this paper, named GRSS-Net. The proposed method utilizes hyperspectral point data as auxiliary data to provide spectral reference information. Thus, the spectral super-resolution can extend the spectral reconstruction range according to spectral data. The proposed method utilizes compressed sensing theory as a fundamental physical mechanism and then unfolds the traditional hyperspectral image reconstruction optimization problem into a deep network. Finally, a high-spatial-resolution hyperspectral image can be obtained. Thus, the proposed method combines the non-linear feature extraction ability of deep learning and the interpretability of traditional physical models simultaneously. A series of experiments demonstrates that the proposed method can effectively reconstruct spectral information in RGB images. Meanwhile, the proposed method provides a framework of spectral super-resolution for different applications. [ABSTRACT FROM AUTHOR]

Hyperspectral images can assist change-detection methods in precisely identifying differences in land cover in the same region at different observation times. However, the difficulty of labeling hyperspectral images restricts the number of training samples for supervised change-detection methods, and there are also complex real influences on hyperspectral images, such as noise and observation directions. Furthermore, current deep-learning-based change-detection methods ignore the feature reusage from receptive fields with different scales and cannot effectively suppress unrelated spatial–spectral dependencies globally. To better handle these issues, a contrastive self-supervised two-domain residual attention network (TRAMNet) with a random augmentation pool is proposed for hyperspectral change detection. The contributions of this article are summarized as follows. (1) To improve the feature extraction from hyperspectral images with random Gaussian noise and directional information, a contrastive learning framework with a random data augmentation pool and a soft contrastive loss function (SCLF) is proposed. (2) The multi-scale feature fusion module (MFF) is provided to achieve feature reusage from different receptive fields. (3) A two-domain residual attention (TRA) block is designed to suppress irrelated change information and extract long-range dependencies from both spectral and spatial domains globally. Extensive experiments were carried out on three real datasets. The results show that the proposed TRAMNet can better initialize the model weights for hyperspectral change-detection task and effectively decrease the need for training samples. The proposed method outperforms most existing hyperspectral change-detection methods. [ABSTRACT FROM AUTHOR]

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-PS 2 ANet) is proposed. The contributions of this article are summarized as follows: (1) a parallel spectral–spatial attention module ( PS 2 A ) 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 PS 2 A can enhance significant information effectively and the FRL can optimize the class-oriented feature distribution. The proposed method outperforms most existent change detection methods. [ABSTRACT FROM AUTHOR]