# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 1 Introduction  
Hyperspectral images (HSIs) are composed of hundreds of continuous narrow spectral bands that cover the visible band to Near Infrared (NIR) or Short Wave Infrared (SWIR) range [1]. These images exhibit unique spectral characteristics due to their continuous spectrum curves. Each land cover within the HSI has its own distinct continuous spectral curve, enabling the identification of subtle differences between different land covers. Therefore, HSIs find extensive applications in various fields, such as target detection, change detection and anomaly detection. The purpose of hyperspectral anomaly detection is to identify objects whose spectrum are significantly different from the surrounding background. The spectral features of anomalies in the HSI differ significantly from those of the surrounding pixels, and the entire image excluding the anomalies is considered as the background [2]. Hyperspectral anomaly detection typically falls under the category of unsupervised binary classification since it does not require prior knowledge or a priori information about the target or the background. Since the spectral information of many land covers cannot be known in advance, hyperspectral anomaly detection is highly relevant to real-world scnarios and has been successfully applied in various fields, such as civil search and rescue, precision agriculture, and mineral exploration [3; 4; 5]. Over the past decades, many hyperspectral anomaly detection algorithms have been proposed, which can be roughly classified into four categories: statistical-based algorithms, representation-based algorithms, matrix decomposition-based algorithms, and deep learning-based algorithms [6]. The RX algorithm, short for Reed-Xiaoli (RX) algorithm, is a classic statistical-based algorithm that assumes a Gaussian distribution for the background. It detects anomalies by calculating the spectral difference between the target pixel and the background pixel using the Mahalanobis distance. However, the RX algorithm calculates the covariance matrix for the entire image, which means it is influenced by the anomalous pixels in the overall background when calculating the Mahalanobis distance for a single pixel point. To address this issue, the Local RX (LRX) algorithm [7] employs a two-window sliding strategy. It extracts the background for each sliding window and calculates the covariance matrix specifically for that window. The Mahalanobis distance is then calculated within the sliding window to detect anomalies. Thus, the LRX algorithm can alleviate the problem that the RX algorithm is susceptible to other background. [PERSON] and [PERSON] [8] conducted a comparison of the RX algorithm based on different distances, namely the Bhattacharya distance, KL divergence, and Jeffrey divergence, to assess their impact on different spectral bands. [PERSON] et al. [9] proposed a nonlinear anomaly detection algorithm based on the RX algorithm and reproducing kernel theory. It should be noted that the RX algorithm assumes that the background obeys a multivariate Gaussian distribution [10; 11], but HSIs in real scenes do not satisfy this assumption. The representation-based algorithms assume that the background pixel in a HSI can be approximated using its surrounding pixels or a background dictionary, while anomalous pixels cannot be effectively represented. As a result, representation-based algorithms do not rely on assumptions about the distribution of HSIs, making them more suitable for real-world scenarios. Representation-based algorithms mainly contain algorithms based on a collaborative-based detector [12; 13] and algorithms based on sparse representation [14]. The hyperspectral anomaly algorithm based on Low-rank and Sparse Matrix Decomposition (LRaSMD) [15] is a representative matrix decomposition-based algorithm. It assumes that the background in HSIs has a low-rank property and the anomalous target has a sparse property [16].