Hyperspectral Anomaly Detection with Auto-Encoder and Independent Target

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 22; p. 5266
• Main Authors: Chen, Shuhan, Li, Xiaorun, Yan, Yunfeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: Algorithms; Anomalies; Artificial intelligence; auto-encoder; Coders; Collaboration; Comparative analysis; Construction; Decomposition; detectors; Dictionaries; Image processing; independent target subspace (ITS); low rank and sparse decomposition (LRaSMD); Methods; Multispectral photography; Neural networks; neurons; Normal distribution; Reconstruction; remote sensing; Sensors; Sparse matrices; sparsity cardinality (SC); Technology application; unsupervised independent target; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15225266

As an unsupervised data representation neural network, auto-encoder (AE) has shown great potential in denoising, dimensionality reduction, and data reconstruction. Many AE-based background (BKG) modeling methods have been developed for hyperspectral anomaly detection (HAD). However, their performance is subject to their unbiased reconstruction of BKG and target pixels. This article presents a rather different low rank and sparse matrix decomposition (LRaSMD) method based on AE, named auto-encoder and independent target (AE-IT), for hyperspectral anomaly detection. First, the encoder weight matrix, obtained by a designed AE network, is utilized to construct a projector for generating a low-rank component in the encoder subspace. By adaptively and reasonably determining the number of neurons in the latent layer, the designed AE-based method can promote the reconstruction of BKG. Second, to ensure independence and representativeness, the component in the encoder orthogonal subspace is made into a sphere and followed by finding of unsupervised targets to construct an anomaly space. In order to mitigate the influence of noise on anomaly detection, sparse cardinality (SC) constraint is enforced on the component in the anomaly space for obtaining the sparse anomaly component. Finally, anomaly detector is constructed by combining Mahalanobi distance and multi-components, which include encoder component and sparse anomaly component, to detect anomalies. The experimental results demonstrate that AE-IT performs competitively compared to the LRaSMD-based models and AE-based approaches.