By merging high-resolution panchromatic (PAN) images with low-resolution multispectral (MS) images, high-resolution MS images with complementary information can be obtained, i.e., pansharpening. Multiresolution analysis (MRA) methods have attracted widespread attention in the pansharpening field. The spatial detail information injected into MS images is extracted from PAN images by MRA tools. Since such methods often suffer from spatial distortion and ringing artifacts, a restoration algorithm based on blind deblurring and iterative back-projection (IBP) is proposed in this paper. First, a blind deblurring method based on the Tikhonov regular constraint model is used to estimate the blurring filter. Second, spatial details extracted from PAN images are modulated into MS images using a high-pass modulation (HPM) framework, and then fusion images are spatially enhanced based on the modulation results and blurring filter. Finally, the IBP technique is used to project the reconstruction error back to iteratively update and optimize the desired high-resolution images. Experiments are performed on data sets acquired by different satellites at full and reduced resolution, and eight state-of-the-art MRA-based pansharpening methods are used for validation. Compared to the enhanced back-projection (EBP) algorithm, the proposed restoration method is better in improving spectral and spatial quality of MRA-based pansharpening. The results indicate the effectiveness and superiority of the proposed method.

Hyperspectral anomaly detection is a "blind" detection of abnormal targets in a region based on spectral and spatial information under the premise of no prior knowledge of the target, which is a very important research topic in the field of remote sensing. In the anomaly detection of hyperspectral images, the salient feature map and spatial-spectral features are not effectively used, which greatly limits the detection performance. To solve the above problems, this paper proposes a hyperspectral anomaly detection method based on intrinsic image decomposition and background subtraction. Firstly, the optimal clustering framework (OCF) is used to select the appropriate bands as the subsequent input images. Secondly, the hyperspectral visual attention model (HVAM) is applied to extract the salient feature map of the image, and the initial anomaly detection map can be obtained by morphological filtering and background subtraction. Then, the pure spectral information in the reflection component of the hyperspectral image is obtained by the intrinsic image decomposition, and the adaptive weight map is calculated on the reflectance image by the spectral angle distance. Finally, the weight map is fused with the initial anomaly detection map to obtain the final anomaly detection result. In the experimental, the proposed method is compared with eleven anomaly detection methods, which including GRXD, RPCA-RX, LSMAD, LRASR, GTVLRR, PTA, HAD-LEBSR, TPCA, PCA-TLRSR, VABS, and GNLTR. The results demonstrate that the proposed method can better enhance the separability of background and anomaly target, so it improves the accuracy of anomaly detection.

In order to improve the fusion quality of multispectral (MS) and panchromatic (PAN) images, a pansharpening method with a gradient domain guided image filter (GIF) that is based on non-subsampled shearlet transform (NSST) is proposed. First, multi-scale decomposition of MS and PAN images is performed by NSST. Second, different fusion rules are designed for high- and low-frequency coefficients. A fusion rule that is based on morphological filter-based intensity modulation (MFIM) technology is proposed for the low-frequency coefficients, and the edge refinement is carried out based on a gradient domain GIF to obtain the fused low-frequency coefficients. For the high-frequency coefficients, a fusion rule based on an improved pulse coupled neural network (PCNN) is adopted. The gradient domain GIF optimizes the firing map of the PCNN model, and then the fusion decision map is calculated to guide the fusion of the high-frequency coefficients. Finally, the fused high- and low-frequency coefficients are reconstructed with inverse NSST to obtain the fusion image. The proposed method was tested using the WorldView-2 and QuickBird data sets; the subjective visual effects and objective evaluation demonstrate that the proposed method is superior to the state-of-the-art pansharpening methods, and it can efficiently improve the spatial quality and spectral maintenance.

In order to improve the spatial resolution of multispectral (MS) images and reduce spectral distortion, a segmentation-cooperated pansharpening method using local adaptive spectral modulation (LASM) is proposed in this paper. By using the k-means algorithm for the segmentation of MS images, different connected component groups can be obtained according to their spectral characteristics. For spectral information modulation of fusion images, the LASM coefficients are constructed based on details extracted from images and local spectral relationships among MS bands. Moreover, we introduce a cooperative theory for the pansharpening process. The local injection coefficient matrix and LASM coefficient matrix are estimated based on the connected component groups to optimize the fusion result, and the parameters of the segmentation algorithm are adjusted according to the feedback from the pansharpening result. In the experimental part, degraded and real data sets from GeoEye-1 and QuickBird satellites are used to assess the performance of our proposed method. Experimental results demonstrate the validity and effectiveness of our method. Generally, the method is superior to several classic and state-of-the-art pansharpening methods in both subjective visual effect and objective evaluation indices, achieving a balance between the injection of spatial details and maintenance of spectral information, while effectively reducing the spectral distortion of the fusion image.

Hyperspectral (HS) pansharpening aims to fuse a low spatial resolution HS image with a registered high spatial resolution panchromatic (PAN) image to generate the enhanced HS image with both high spatial and spectral resolution. Existing pansharpening approaches based on convolutional neural networks (ConvNets) are poorly interpretable and rarely use attention mechanisms to extract relevant texture details. In this paper, we present a novel HS pansharpening method based on detail injection framework and HyperTransformer. Firstly, the feature extraction sub-network is used to extract the texture and spectral information of the PAN and HS images, secondly, we designed the spatial-spectral fusion sub-network based on HyperTransformer to obtain the detail information, in which the cross-feature space dependency between PAN and low resolution HS features is extracted and the high-frequency details with more spectrally similar features are learned by the attention mechanism. Finally, the detail injection framework is applied to fuse the detail information and the low-frequency component to obtain the final high-resolution HS image. Comparative experiments conducted on three widely used datasets demonstrate the superiority of the proposed method over the state-of-the-art pansharpening results in terms of both subjective visual effects and quantitative evaluations.

Aimed at the lack of the spectral information preservation and the spatial detail injection in fusion of multispectral (MS) and panchromatic (PAN) images, the paper proposed a pansharpening algorithm based on convolutional sparse representation (CSR) and morphological filter (MF) by introducing a recently emerged signal decomposition model known as CSR. Firstly, the PAN and MS images are decomposed to obtain a base layer and a detail layer, respectively. Secondly, the fusion rule of the base layers which based on MF and high-pass modulation (HPM) scheme is proposed to retain more details. For the fusion of detail layers, maximum selection scheme based on activity maps and CSR model are adopted for fusion. Finally, the fusion results of the base layer and detail layer are reconstructed to obtain the final fusion image. The experimental results show that the proposed method is superior to the traditional methods and some current popular fusion methods from the visual effects and the objective indices.