Hyperspectral anomaly detection is aimed at detecting observations that differ from their surroundings, and is an active area of research in hyperspectral image processing. Recently, autoencoders (AEs) have been applied in hyperspectral anomaly detection; however, the existing AE-based methods are complicated and involve manual parameter setting and preprocessing and/or postprocessing procedures. In this article, an autonomous hyperspectral anomaly detection network (Auto-AD) is proposed, in which the background is reconstructed by the network and the anomalies appear as reconstruction errors. Specifically, through a fully convolutional AE with skip connections, the background can be reconstructed while the anomalies are difficult to reconstruct, since the anomalies are relatively small compared to the background and have a low probability of occurring in the image. To further suppress the anomaly reconstruction, an adaptive-weighted loss function is designed, where the weights of potential anomalous pixels with large reconstruction errors are reduced during training. As a result, the anomalies have a higher contrast with the background in the map of reconstruction errors. The experimental results obtained on a public airborne data set and two unmanned aerial vehicle-borne hyperspectral data sets confirm the effectiveness of the proposed Auto-AD method.

Hyperspectral anomaly detection is aimed at detecting observations that differ from their surroundings. To achieve this goal, low-rank models and autoencoders (AEs) have attracted a lot of attention. Although the low-rank model is self-explainable, a low-rank prior may not completely match real data. In contrast, AEs can automatically learn the discriminative features between anomalies and background, whereas AEs are not self-explainable. In this article, a deep low-rank prior-based method (DeepLR) is proposed, which combines a model-driven low-rank prior and a data-driven AE. To be specific, the low-rank prior and a fully convolutional AE architecture are incorporated through modeling an energy minimization problem solved by an iterative optimization framework, in which low-rank background estimation and network training serve as two subproblems. The low-rank background is input into the network to calculate a low-rank regularized loss, constraining the training of the network. Finally, the background can be approximately reconstructed, while the anomalies are reconstructed with significant reconstruction errors; thus, the reconstruction errors indicate the anomalous degree. The experimental results obtained on several public datasets and two large unmanned aerial vehicle (UAV)-borne datasets confirm the merit and viability of the proposed method.

Anomaly detection is an active area of research in hyperspectral information processing. Recently, low-rank representation has been applied in hyperspectral anomaly detection. However, the existing low-rank-based methods either involve a complicated dictionary construction process or the anomaly-background separation which is not sufficient. In this article, to solve these problems, a novel hyperspectral anomaly detection method based on a locally enhanced low-rank prior (LELRP-AD) is proposed. This article is inspired by the observation that, in local homogeneous regions, the background signals hold an enhanced low-rank property while the anomalies exhibit spatial sparsity. Based on this observation, the background pixels can be low-rank reconstructed by a set of basis background signals, whereas anomalies can be represented as sparse residuals. First, image segmentation is performed to enhance the homogeneity of the background, in which a Potts-based image segmentation algorithm is adopted with postprocessing, thus avoiding the need for a complicated spectral dictionary for the representation of the background. Furthermore, the original hyperspectral data matrix is augmented with extracted background endmembers for the low-rank and sparse matrix decomposition, to further achieve anomaly-background separation. The experimental results obtained on four real hyperspectral data sets demonstrate the merit and viability of the proposed method compared with the current state-of-the-art methods.

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 abnormal 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.

Although hyperspectral anomaly detection is commonly conducted in the visible, near-infrared, and shortwave infrared spectral regions, there has been less research on hyperspectral anomaly detection in the longwave infrared (LWIR) hyperspectral region. The radiance of thermal infrared hyperspectral imagery is determined by the temperature and emissivity. To avoid the detection uncertainty caused by the single factor of temperature, emissivity can be introduced to detect anomalies. However, in the emissivity domain, the spectral contrast and signal-to-noise ratio (SNR) are low, which makes it difficult to separate the anomalies from the background. In this paper, an anomaly detection method combining emissivity and a segmented low-rank prior (EaSLRP) is proposed for use with thermal infrared hyperspectral imagery. The EaSLRP method is divided into three parts—1) temperature/emissivity retrieval, 2) extraction of the thermal infrared hyperspectral background information, and 3) Mahalanobis distance detection. A homogeneous region generation method is also proposed to solve the problem of the complex global background leading to inaccurate background estimation. The GoDec method is used for matrix decomposition and background information extraction and to remove some of the noise. The proposed Mahalanobis distance detector then uses the background component and original image for anomaly detection, while highlighting the spectral difference between the anomalies and background. This method can also suppress the influence of noise, to some extent. The experimental results obtained with airborne Fourier transform thermal infrared spectrometer hyperspectral images demonstrate that the EaSLRP method is effective when compared with the Reed–Xiaoli detector (RXD), the segmented RX detector (SegRX), the low-rank and sparse representation-based detector (LRASR), the low-rank and sparse matrix decomposition (LRaSMD)-based Mahalanobis distance method (LSMAD), and the locally enhanced low-rank prior method (LELRP-AD).

Achieving reliable in-season soybean maps is challenging in heterogeneous and data-pooragricultural landscapes because of domain discrepancies and limited reference data. Traditionalvegetation index methods and supervised machine-learning approaches often lack robustnessfor early-season prediction, while conventional unsupervised domain adaptation (UDA)typically requires access to source-domain data, increasing computational and data-sharingburdens. In this study, we introduce a source-free UDA framework, Contrastive RepresentationOptimized Prototype Segmentation (CROPS), for large-scale, early-season soybean mappingwithout relying on source data. CROPS utilizes NDVI-Max composites from theQualityMosaic method to emphasize peak vegetation signals, reduce noise and redundancy, andsimplify preprocessing. A pixel-wise entropy partitioning strategy identifies high- and low-confidence regions, enabling curriculum-based optimization within a teacher-studentarchitecture enhanced by Exponential Moving Average (EMA). Extensive experiments acrossthe USA, China, Brazil, and Argentina demonstrate that CROPS consistently surpassestraditional indices, supervised classifiers, and established UDA methods. At the end of theseason, CROPS achieved average macro F1 scores exceeding 92%, closely matching officialagricultural statistics. Importantly, in South America, CROPS enables reliable early-seasonmapping, with macro F1 above 90% in Brazil by mid-January and over 80% in Argentina by lateJanuary. In the US Midwest, where the spectral similarity between soybean and maize makesaccurate classification particularly challenging during the growing season, CROPS achievesrobust in-season mapping for both crops. Ablation experiments reveal that this strongperformance is primarily attributed to the NDVI-Max composites’ ability to capture keyphenological features and to the progressive self-adaptive learning process, in which high-confidence target-domain samples iteratively guide the low-confidence ones. This strategyavoids negative transfer from source data and enhances adaptation to local characteristics.These findings underscore the potential of CROPS as a timely, accurate, and scalable solutionfor crop mapping in complex, data-limited environments.