目的 自编码器作为一种无监督的特征提取算法，可以在无标签的条件下学习到样本的高阶、稠密特征。然而当训练集含噪声或异常时，会迫使自编码器学习这些异常样本的特征，导致性能下降。同时，自编码器应用于高光谱图像处理时，往往会忽略掉空域信息，进一步限制了自编码器的探测性能。针对上述问题，本文提出一种基于空域协同自编码器的高光谱异常检测算法。方法 利用块图模型优良的背景抑制能力从空域角度筛选用于自编码器训练的背景样本集。自编码器采用经预筛选的训练样本集进行网络参数更新，在提升对背景样本表达能力的同时避免异常样本对探测性能的影响。为进一步将空域信息融入探测结果，利用块图模型得到的异常响应构建权重，起到突出目标并抑制背景的作用。结果 实验在3组不同尺寸的高光谱数据集上与5种代表性的高光谱异常检测算法进行比较。本文方法在3组数据集上的AUC （area under the curve）值分别为0.990 4、0.988 8和0.997 0，均高于其他算法。同时，对比了不同的训练集选择策略，与随机选取和使用全部样本进行对比。结果表明，本文基于空域响应的样本筛选方法相较对比方法具有较明显的优势。结论 提出的基于空域协同自编码器的高光谱异常检测算法从空域角度筛选样本以提升自编码器区分异常与背景的能力，同时融合了光谱域和空域信息，进一步提升了异常检测性能。

Objective Hyperspectral imagery (HSI) consists of hundreds of narrow spectral bands and provides richer spectral information than infrared and multispectral images. Its features can distinguish targets from the background, and has been widely applied in many remote-sensing tasks, such as intelligent agriculture and mineral exploration. In many circumstance, however, targets are hard to be detected because of the existed prior spectral information is related to the background or targets. Anomaly detection can detect potential targets that differ from the surrounding background in an unsupervised manner. Anomalies focus on small scaled manual objects embedded in the surrounding background, and they differ from the background in terms of spectral information. As a typical unsupervised non-linear feature extractor, autoencoder (AE) contexts are applied in hyperspectral anomaly detection tasks. AE-based anomaly detection framework is getting more and more attention and many algorithms are developed to improve the detection performance. However, those AE-based anomaly detectors are hard to distinguish anomalies from the background due to lots of noise or outliers in training set. To minimize the objective function, the AE tends to learn the features of these noise and outliers. We facilitate spatial-coordinated autoencoder (ScAE) tackle these two issues mentioned above. Method Thanks to infrared patch-image model in suppressing the background and highlighting weak targets, a patch-image model is designed for hyperspectral images to be introduced in ScAE. Specifically, the patches are extended to form a matrix based on the first three components of HSI, the low-rank and sparse matrix decomposition (LRaSMD) is developed. The sparse part contains the target information and the background is suppressed. Thus, the training samples are opted based on the response of the decomposed sparse part, pixels with lower values and vice versa. The picked training samples are more clearly than utilizing the entire dataset or random selection strategy, and these samples are employed to update the weights of AE networks. The traditional gradients descend method and the backward propagation strategy is used to fine-tune AE. After some iterations, the vanilla AE is upgraded to be an effective hyperspectral anomaly detector. The reconstruction residual is utilized as the criterion to determine whether a pixel under the test (PUT) is anomalous or not. In order to take full advantage of the spatial information, a spatial-responses non-negative weight of patch-image model is introduced to increase the discrimination between the background and anomalies further, and the final detection result is obtained via fusing the spectral result and the weight matrix with Hadamard product. Result We evaluate our ScAE detector on three challenging hyperspectral image datasets with different size, namely Sandiego-1, Sandiego-2 and Botanical Garden. First, we design a set of experiment to figure out the issue of influential parameters for the final detection result. There are three parameters which are closely related with the detection performance. After extensive experiments, we found that ScAE is cohesive to the trade-off parameter and patch-related parameter, but is less to the number of hidden layers. Then, we conduct an ablation study to clarify the feasibility of the ScAE framework. The detection performance illustrates that patch-image model is optimal in suppressing the background while highlighting weak anomalies compared to attribute filter (AF). Our selection strategy gains higher area under the curve (AUC) values than randomly selection strategy. It is worth noting that a weight matrix related spatial and spectral information fusion can improve the detection result, as AUC values demonstrated. Finally, we compare our ScAE detector to three classical anomaly detectors and two popular hyperspectral anomaly detector, namely global Reed-Xiaoli (GRX), LRaSMD-based Mahalanobis distance method (LSMAD), traditional AE, fractional Fourier entropy (FrFE) and feature extraction and background purification anomaly detector (FEBPAD), respectively. Such parameters are set to get optimal detection performance for the six detectors all. The corresponding AUC values of ScAE are 0.990 4, 0.988 8 and 0.997 0 for Sandiego-1, Sandiego-2 and Botanical Garden, respectively. The detection result illustrates that ScAE obtains the highest AUC values amongst the six hypersepctral anomaly detectors. Moreover, ScAE generates the lowest false alarm rate (FAR) when all of the anomalous pixels are detected in three datasets all. Conclusion A novel AE-based hyperspectral anomaly detection algorithm is developed that spectral-detected spatial information can enhance the segmentation ability between anomalies and the background. The comparative experiments demonstrate our ScAE detector has its feasibility and potentials for hyperspectral anomaly detection.