One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning

Hyperspectral anomaly detection (HAD) involves identifying the targets that deviate spectrally from their surroundings, without prior knowledge. Recently, deep learning based methods have become the mainstream HAD methods, due to their powerful spatial-spectral feature extraction ability. However, the current deep detection models are optimized to complete a proxy task (two-step paradigm), such as background reconstruction or generation, rather than achieving anomaly detection directly. This leads to suboptimal results and poor transferability, which means that the deep model is trained and tested on the same image. In this paper, an unsupervised transferred direct detection (TDD) model is proposed, which is optimized directly for the anomaly detection task (one-step paradigm) and has transferability. Specially, the TDD model is optimized to identify the spectral deviation relationship according to the anomaly definition. Compared to learning the specific background distribution as most models do, the spectral deviation relationship is universal for different images and guarantees the model transferability. To train the TDD model in an unsupervised manner, an anomaly sample simulation strategy is proposed to generate numerous pairs of anomaly samples. Furthermore, a global self-attention module and a local self-attention module are designed to help the model focus on the "spectrally deviating" relationship. The TDD model was validated on four public HAD datasets. The results show that the proposed TDD model can successfully overcome the limitation of traditional model training and testing on a single image, and the model has a powerful detection ability and excellent transferability.

Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors

Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications. However, it is a challenging task due to the complex distribution and the irregular shapes of objects, and the lack of abnormal samples. To tackle these problems, an anomaly segmentation model based on pixel descriptors (ASD) is proposed for anomaly segmentation in HSR imagery. Specifically, deep one-class classification is introduced for anomaly segmentation in the feature space with discriminative pixel descriptors. The ASD model incorporates the data argument for generating virtual ab-normal samples, which can force the pixel descriptors to be compact for normal data and meanwhile to be diverse to avoid the model collapse problems when only positive samples participated in the training. In addition, the ASD introduced a multi-level and multi-scale feature extraction strategy for learning the low-level and semantic information to make the pixel descriptors feature-rich. The proposed ASD model was validated using four HSR datasets and compared with the recent state-of-the-art models, showing its potential value in Earth vision applications.

Stabilizing Deep Tomographic Reconstruction Networks

Tomographic image reconstruction with deep learning is an emerging field of applied artificial intelligence but a recent study reveals that deep reconstruction networks, such as well-known AUTOMAP, are unstable for computed tomography (CT) and magnetic resonance imaging (MRI). Specifically, three kinds of instabilities were identified: (1) strong output artefacts from tiny perturbation, (2) poor detection of small features, and (3) decreased performance with increased input data. These instabilities are believed to be from lacking kernel awareness and nontrivial to overcome, but compressed sensing (CS) reconstruction was reported to be stable due to its kernel awareness. Since deep reconstruction may potentially become the main driving force to achieve better image quality, stabilizing deep reconstruction networks is an urgent challenge. Here we propose an Analytic, Compressive, Iterative Deep (ACID) network to fundamentally address this challenge. Instead of only using deep learning or compressed sensing, ACID consists of four modules including deep reconstruction, CS, analytic mapping, and iterative refinement. In our experiments, ACID eliminated all three kinds of instabilities and significantly improved image quality relative to the methods in the aforementioned PNAS study. ACID is only an example of integrating diverse algorithmic ingredients but it has clearly demonstrated that data-driven reconstruction can be stabilized to outperform reconstruction using CS alone. The power of ACID comes from a unique combination of a deep reconstruction network trained on big data, CS via advanced optimization, and iterative refinement that stabilizes the whole workflow. We anticipate that this integrative closed-loop data driven approach will add great value to clinical and other applications.