Limited by the anomalous spectral vectors in unlabeled hyperspectral images (HSIs), anomaly detection methods based on background distribution estimation often suffer from the contamination of anomalies, which decreases the estimation accuracy and, thus, weakens the detection performance. To address this problem, we proposed a novel semisupervised spectral learning (SSL) for the hyperspectral anomaly detection framework based on the generative adversarial network (GAN). GAN is applied and developed to estimate the background distribution in a semisupervised manner and obtain an initial spectral feature because of its strong representational capability and adversarial training advantage. In the proposed framework, an initial spatial feature is generated via morphological attribute filtering. Finally, an exponential constrained nonlinear suppression fusion technique is adopted to suppress the background and combine the complementary information in different features to obtain a fused detection map. The performance of the proposed anomaly detection technique is evaluated on a series of HSIs. Experimental results demonstrate that our method can outperform state-of-the-art anomaly detection methods.

Hyperspectral anomaly target detection (also known as hyperspectral anomaly detection (HAD)] is a technique aiming to identify samples with atypical spectra. Although some density estimation-based methods have been developed, they may suffer from two issues: 1) separated two-stage optimization with inconsistent objective functions makes the representation learning model fail to dig out characterization customized for HAD and 2) incapability of learning a low-dimensional representation that preserves the inherent information from the original high-dimensional spectral space. To address these problems, we propose a novel end-to-end local invariant autoencoding density estimation (E2E-LIADE) model. To satisfy the assumption on the manifold, the E2E-LIADE introduces a local invariant autoencoder (LIA) to capture the intrinsic low-dimensional manifold embedded in the original space. Augmented low-dimensional representation (ALDR) can be generated by concatenating the local invariant constrained by a graph regularizer and the reconstruction error. In particular, an end-to-end (E2E) multidistance measure, including mean-squared error (MSE) and orthogonal projection divergence (OPD), is imposed on the LIA with respect to hyperspectral data. More important, E2E-LIADE simultaneously optimizes the ALDR of the LIA and a density estimation network in an E2E manner to avoid the model being trapped in a local optimum, resulting in an energy map in which each pixel represents a negative log likelihood for the spectrum. Finally, a postprocessing procedure is conducted on the energy map to suppress the background. The experimental results demonstrate that compared to the state of the art, the proposed E2E-LIADE offers more satisfactory performance.

Multi-modal fusion holds great promise for integrating information from different modalities. However, due to a lack of consideration for modal consistency, existing multi-modal fusion methods in the field of remote sensing still face challenges of incomplete semantic information and low computational efficiency in their fusion designs. Inspired by the observation that the visual language pre-training model CLIP can effectively extract strong semantic information from visual features, we propose M³amba, a novel end-to-end CLIP-driven Mamba model for multi-modal fusion to address these challenges. Specifically, we introduce CLIP-driven modality-specific adapters in the fusion architecture to avoid the bias of understanding specific domains caused by direct inference, making the original CLIP encoder modality-specific perception. This unified framework enables minimal training to achieve a comprehensive semantic understanding of different modalities, thereby guiding cross-modal feature fusion. To further enhance the consistent association between modality mappings, a multi-modal Mamba fusion architecture with linear complexity and a cross-attention module Cross-SS2D are designed, which fully considers effective and efficient information interaction to achieve complete fusion. Extensive experiments have shown that M³amba has an average performance improvement of at least 5.98% compared with the state-of-the-art methods in multi-modal hyperspectral image classification tasks in the remote sensing field, while also demonstrating excellent training efficiency, achieving a double improvement in accuracy and efficiency. The code is released at https://github.com/kaka-Cao/M3amba.

Neurologically, filter pruning is a procedure of forgetting and remembering recovering. Prevailing methods directly forget less important information from an unrobust baseline at first and expect to minimize the performance sacrifice. However, unsaturated base remembering imposes a ceiling on the slimmed model leading to suboptimal performance. And significantly forgetting at first would cause unrecoverable information loss. Here, we design a novel filter pruning paradigm termed Remembering Enhancement and Entropy-based Asymptotic Forgetting (REAF). Inspired by robustness theory, we first enhance remembering by over-parameterizing baseline with fusible compensatory convolutions which liberates pruned model from the bondage of baseline at no inference cost. Then the collateral implication between original and compensatory filters necessitates a bilateral-collaborated pruning criterion. Specifically, only when the filter has the largest intra-branch distance and its compensatory counterpart has the strongest remembering enhancement power, they are preserved. Further, Ebbinghaus curve-based asymptotic forgetting is proposed to protect the pruned model from unstable learning. The number of pruned filters is increasing asymptotically in the training procedure, which enables the remembering of pretrained weights gradually to be concentrated in the remaining filters. Extensive experiments demonstrate the superiority of REAF over many state-of-the-art (SOTA) methods. For example, REAF removes 47.55% FLOPs and 42.98% parameters of ResNet-50 only with 0.98% TOP-1 accuracy loss on ImageNet. The code is available at https://github.com/zhangxin-xd/REAF.

Hyperspectral anomaly detection (HAD) aims to identify samples with unknown atypical spectra from the background. Deep learning (DL)-based methods, particularly autoencoders (AEs), have proven effective in uncovering the underlying profiles for HAD. However, in real-world applications of hyperspectral images (HSIs), complex background land-covers and anomaly corruptions are common, leading to two issues: 1) A low-dimensional manifold characterized by DL-based HAD methods can only reveal a few underlying variation factors of the background distribution and cannot capture the complex structures behind land-covers of all categories. 2) DL-based HAD methods trained on anomaly-contaminated HSIs tend to overfit specific anomalies, resulting in poor background characterization. To tackle these issues, this study presents a novel and robust framework for HAD called Model-Driven Deep Mixture Network (MDMN) that combines the strengths of model-driven and data-driven approaches while emphasizing interpretability. By assuming that the background, consisting of various land-covers, arises from a mixture of low-dimensional manifolds, the MDMN incorporates a novel deep mixture module to comprehensively characterize the background. This module utilizes a low-dimensional manifold learned by an AE to represent a specific category of background land-covers. To mitigate the impact of anomaly corruptions, the MDMN incorporates a convex relaxation of a sparse constraint, which helps prevent overfitting anomalies. Extensive experimental results demonstrate that the proposed MDMN offers more satisfactory and robust detection performance.