# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 1 Introduction
The LRaSMD algorithm employs matrix decomposition to obtain a sparse matrix and determines anomalous targets by calculating the Euclidean distance within the sparse matrix. [PERSON] et al. [17] proposed an enhanced tensor RPCA-based Mahalanobis distance algorithm, which utilizes weighted tensor Schatten p-norm minimization and solves non-convex problems using Fourier transform, generalized soft-threshold, and a tensor singular value decomposition to address decomposition issues. In recent years, deep learning techniques have gained significant attention for their promising detection performance in hyperspectral anomaly detection. Deep learning-based algorithms can be broadly classified into three major categories: convolutional neural network-based, generative adversarial network-based, and auto-encoder-based algorithms. Convolutional neural network-based algorithms train neural networks to distinguish the differences between adjacent pixels and utilize the trained network to detect anomalies based on pixel differences [18]. Generative adversarial network-based algorithms remove the background from HSIs by subtracting synthetic images from the original image, then detect anomalies based on the acquired spectral difference images [19]. The auto-encoder-based algorithms consists of an encoder and a decoder. Since the majority of the input data to the auto-encoder network are background pixels, the network learns patterns represent the background. The decoding and reconstruction of background pixels yield a low reconstruction error, while anomalous targets exhibit a considerably higher reconstruction error. This distinction serves as an effective method for detecting anomalies [20]. [PERSON] and [PERSON] [21] proposed a memory-augmented auto-encoder for hyperspectral anomaly detection, where the latent representation from the auto-encoder is used to retrieve the most relevant matrix items in a memory matrix. Although the auto-encoder network can better extract features from HSIs [22], it does not pay attention to the effective bands of land covers during the feature extraction process. Each land cover within HSIs exhibits different effective bands, and incorporating an attention mechanism can enhance the effectiveness of the extracted features by emphasizing the important bands for each land cover. Moreover, the performance of the LRX algorithm is easily affected by noise, while HSIs have high noise because of the low signal-to-noise ratio in hyperspectral sensors, environmental influences, and technical limitations during image acquisition. Due to the influence of noise, the direct application of the LRX method for anomaly detection on HSIs does not yield satisfactory results. Therefore, this paper proposes a hyperspectral anomaly detection algorithm based on the channel attention mechanism and LRX. The HSI is fed into the auto-encoder network that is constrained by the channel attention module to generate a more representative reconstructed image that better captures the characteristics of the land covers. The channel attention module in the auto-encoder network aims to explore the effective spectral bands corresponding to different land covers. Subsequently, the LRX algorithm is utilized for anomaly detection on the reconstructed image obtained from the auto-encoder network with the channel attention mechanism. The main contributions of this paper include:  
1. The channel attention module is proposed to explore the effective bands of HSIs, enhancing the representation of different land covers in the reconstructed image generated by the auto-encoder network. 2. The LRX algorithm is employed for anomaly detection on the reconstructed image, which effectively alleviates the impact of existing noises in HSIs. 3. The effectiveness of the proposed hyperspectral anomaly detection algorithm based on the channel attention model and LRX is verified on three hyperspectral datasets, HYDICE, AVIRIS, and Salinas, with AUC values of 0.9871, 0.9916, and 0.9642, respectively.