Hyperspectral Anomaly Detection With Relaxed Collaborative Representation

Anomaly detection has become an important remote sensing application due to the abundant spectral and spatial information contained in hyperspectral images. Recently, hyperspectral anomaly detection methods based on the collaborative representation (CR) model have attracted significant attention. Nevertheless, these methods have to face two main challenges: 1) all features (spectral signatures) are constrained to share the same representation coefficient, which ignores the differences among features and 2) existing dictionaries for pixel-by-pixel detection models are usually not reliable. To address these issues, this article proposes a new relaxed CR detector for hyperspectral anomaly detection by using a novel nonglobal dictionary. The proposed detector conducts CR on each feature dimension of the pixel under test and simultaneously constrains the coding vectors of different features to be similar. To the best of our knowledge, this is the first time that a detection model is built from each feature dimension. To adjust the contributions of each feature, an adaptive feature weight constrained version of the method is also proposed. The nonglobal dictionary is constructed by combining the k-nearest neighbor method and an existing global dictionary, which is more reliable and practical than the widely used dual-window dictionary. In addition, this article also designs a band selection strategy for the proposed method. Experiments on five real datasets indicate that the proposed method suppresses background well and outperforms other classical and state-of-the-art methods.