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Recently developed deep learning-based hyperspectral anomaly detection (HAD) methods typically include two steps where the deep feature extraction is not designed specifically for the HAD task. In this article, we propose a spectral distribution-aware estimation network (SDEN) that does not conduct feature extraction and anomaly detection in two separate steps but instead learns both jointly to estimate anomalies directly in an end-to-end manner without postprocessing. The unified framework can ensure that the extracted features serve better for anomaly detection. To preserve the distribution of hyperspectral images (HSIs) during dimensionality reduction, the SDEN introduces a spectral distribution (SD)-aware module imposed with a local-invariant constraint. More specifically, we adopt Markov chain Monte Carlo (MCMC) that enables the SD module to better estimate the distribution of the complex HSIs. Considering the powerful representation capability of Gaussian mixture model (GMM), the SDEN leverages it to establish an estimation module in the deep latent space where the anomaly resides in low density while the background not. We demonstrate that the SDEN yields competitive and highly promising results in comparison with the anomaly detection benchmarks. [ABSTRACT FROM AUTHOR]

The fusion of hyperspectral (HS) and panchromatic (PAN) images aims to generate a fused HS image that combines spectral information of the HS image with spatial information of the PAN image. In this article, we propose a multiresolution spatial–spectral feature learning (MSSL) framework for fusing HS and PAN images. The proposed MSSL transforms the existing deep and complex network into several simple and shallow subnetworks to simplify the feature learning process. MSSL upsamples the HS image while downsamples the PAN image and designs multiresolution 3-D convolutional autoencoder (CAEs) networks with a spectral constraint to learn complete spatial–spectral features of the HS image. MSSL designs multiresolution 2-D CAEs with spatial constraint to extract spatial features of the PAN image, with a low computational cost. In order to effectively generate the pansharpened HS image with high spatial and spectral fidelity, a multiresolution residual network is presented to reconstruct the HS image from the extracted spatial–spectral features. Extensive experiments are conducted on three widely used remote sensing data sets in comparison with state-of-the-art HS image fusion methods, demonstrating the superiority of the proposed MSSL method. Code is available at https://github.com/Jiahuiqu/MSSL. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have recently achieved impressive improvements on hyperspectral (HS) pansharpening. However, most of the CNN-based HS pansharpening approaches would have to first upsample the low-resolution hyperspectral image (LR-HSI) using bicubic interpolation or data-driven training strategy, which inevitably lose some details or greatly rely on the learning process. In addition, most previous methods regard the pansharpening as a black-box problem and treat diverse features equally, thus hindering the discriminative ability of CNNs. To conquer these issues, a novel HS pansharpening method using deep hyperspectral prior (DHP) and dual-attention residual network (DARN) is proposed in this article. Specifically, we first upsample the LR-HSI to the scale of the panchromatic (PAN) image through the DHP algorithm, which can better preserve spatial and spectral information without learning from large data sets. The upsampled result is then concatenated with the PAN image to form the input of the DARN, where several channel-spatial attention residual blocks (CSA ResBlocks) are stacked to map the residual HSI between the reference HSI and the upsampled HSI. In each CSA ResBlock, two complementary attention modules, i.e., channel attention and spatial attention modules, are designed to adaptively learn more informative features of spectral channels and spatial locations simultaneously, which can effectively boost the fusion accuracy. Finally, the fused HSI is obtained by the summation of the upsampled HSI and the reconstructed residual HSI. The experimental results of both simulated and real HS data sets demonstrate that the performance of our DHP-DARN method is superior over the state-of-the-art HS pansharpening approaches. [ABSTRACT FROM AUTHOR]