Hyperspectral unmixing (HU) aims to decompose mixed pixels into a set of endmembers and corresponding abundances. Deep learning-based HU methods are currently a hot research topic, but most existing unmixing methods still rely on per-pixel training or employ convolutional neural networks (CNNs), which overlook the non-local correlations of materials and spectral characteristics. Furthermore, current research mainly focuses on linear mixing models, which limits the feature extraction capability of deep encoders and further improvement in unmixing accuracy. In this paper, we propose a nonlinear unmixing network capable of extracting global spatial-spectral features. The network is designed based on an autoencoder architecture, where a dual-stream CNNs is employed in the encoder to separately extract spectral and local spatial information. The extracted features are then fused together to form a more complete representation of the input data. Subsequently, a linear projection-based multi-head self-attention mechanism is applied to capture global contextual information, allowing for comprehensive spatial information extraction while maintaining lightweight computation. To achieve better reconstruction performance, a model-free nonlinear mixing approach is adopted to enhance the model’s universality, with the mixing model learned entirely from the data. Additionally, an initialization method based on endmember bundles is utilized to reduce interference from outliers and noise. Comparative results on real datasets against several state-of-the-art unmixing methods demonstrate the superior of the proposed approach.

Given the problem of spatial detail loss and spectral feature degradation in hyperspectral images (HSIs) characterized as blur, often caused by noise during image acquisition, and methods of removing blur noise designed on HSIs being insufficient, we propose an HSI reconstruction network based on a Blur–Kernel–Prior (BKP) method and Spectral–Spatial Attention (SSA) strategy for noise removal and reconstruction of HSIs. Specifically, a grouping strategy is designed to segment the HSIs into spectral dimension sub-images, and the BKP module, based on U-Net, learns the spatially adaptive blur kernel to extract and remove blurred features from each sub-image while preserving spatial features with spatial resolution. Subsequently, the SSA block is employed to extract shallow features, details, and edge information using a hybrid 2D–3D convolution from the sub-images, followed by deep feature extraction using a deep ResNet and multi-head attention (MSA) on the merged image to maximize the preservation of spectral dimension information. The $L_1$ loss function, combined with spectral dimension loss and peak signal-to-noise ratio loss, is utilized to constrain and ensure reconstruction accuracy. Experiments on both synthetic and real datasets demonstrate that our method exhibits excellent performance in reconstructing HSIs affected by blurred noise, outperforming existing methods in terms of quantitative quality and recovery of spectral dimension information.

Hyperspectral Images (HSIs) classification plays a vital role in precision agriculture and environmental monitoring. However, real-world HSIs is frequently degraded by complex noise, which compromises both spectral integrity and spatial consistency. Existing deep learning (DL) methods, such as Convolutional Neural Networks (CNNs) and Transformers, often face trade-offs between noise suppression and feature preservation, or suffer from high computational costs. To address these challenges, this paper proposes a noise-robust framework named Wavelet-Manifold-Mamba (WMM). Unlike straightforward modular concatenation, WMM establishes a deep coupling between physical-level signal restoration and geometric-level manifold alignment. Specifically, recognizing that the recursive state propagation in Mamba is highly sensitive to input perturbations, a Multi-scale Wavelet Denoising Block (MWDB) is first employed to suppress high-frequency artifacts via learnable cross-frequency attention, thereby stabilizing the token sequence for subsequent modeling. Complementarily, to rectify the feature drift that persists even after denoising, a Manifold-Regularized Mamba Block (MRMB) leverages a topological loss to enforce noise-invariant manifold embeddings. Furthermore, the architecture incorporates a selective scanning mechanism to capture long-range dependencies with linear complexity. Experiments on the WHU-Hi and SZU-Tree datasets demonstrate that the proposed WMM outperforms state-of-the-art methods in noise-robust classification accuracy.

In hyperspectral imaging systems, stripe noise is a common interference phenomenon, severely affecting the quality and usability of data. Although existing denoising methods have achieved certain progress, most algorithms are still built upon specific model assumptions. However, stripe noise in real-world scenarios often exhibits more complex statistical characteristics and unknown distribution patterns. Such model mismatch problems significantly limit the performance of existing methods in practical applications. Leveraging its powerful feature extraction and data generation capabilities, the variational autoencoder (VAE) can effectively capture the underlying distribution structure of data and generate samples highly consistent with real scenes. To address the issue that short-wave infrared hyper spectral images (HSIs) are highly susceptible to stripe noise induced specifically by dark current interference, this paper proposes a novel VAE-based noise modeling and removal method. Specifically, this study utilizes a small number of real noisy images to learn the true distribution characteristics of stripe noise, thereby generating diverse noise samples. By combining the generated noise with existing high-quality standard datasets, a large-scale paired noisy-clean image dataset is constructed, overcoming the limitations of traditional single noise generation models. On this basis, this paper further proposes an improved stripe noise removal model based on a UNet backbone network. Experimental results demonstrate that the proposed method achieves superior performance on both simulated data and real world scenarios, offering a new solution for stripe noise removal in HSIs.