Hyperspectral anomaly detection (HAD) aims to identify targets that are significantly different from their surrounding background, employing an unsupervised paradigm. Recently, detectors based on autoencoder (AE) have become predominant methods and demonstrated satisfactory performance. However, there are still two problems that need to be solved. First, the hypothesis that the AE-based models can effectively reconstruct background samples (BSs) while anomalies cannot, may not always be true in practice, due to their powerful capability for feature extraction. Second, the AE-based models primarily concentrate on the quality of sample reconstruction, regardless of whether the encoded features signify the anomalies or background, which is not conducive to the separation of anomalies from the background. To handle the above-mentioned problems, a novel memory-augmented AE (MAAE) model is developed to better reconstruct the background and suppress anomalies reconstruction. Specifically, for the first problem, a novel superpixel-guided adaptive weight calculation (SAWC) module is devised to generate adaptive weights (AWs) by taking into account contextual information in the error map, and then the AWs are incorporated into the reconstruction loss, where the potential BSs are endowed with larger AWs than anomalies during training. For the second problem, a novel sample attribution mining (SAM) module is developed to mine sample attribution (i.e., explore whether a certain sample belongs to the background or anomaly), and the mined background and anomaly samples (ASs) are employed to train different modules for better separating the anomalies and background. In addition, an entropy-based sparse addressing (ESA) module is further designed to weaken the reconstruction ability of ASs by designing a learnable sparse addressing weight for the memory module. The ablation study validates the effectiveness of the proposed SAWC, SAM, and ESA. Extensive comparison experiments on six hyperspectral image (HSI) datasets demonstrate the superiority in terms of comprehensive detection performance and background suppression of our method.

中文翻译：

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用于高光谱异常检测的具有自适应重建和样​​本属性挖掘的内存增强自动编码器


高光谱异常检测 (HAD) 旨在采用无监督范例来识别与其周围背景显着不同的目标。最近，基于自动编码器（AE）的检测器已成为主要方法并表现出令人满意的性能。然而，仍有两个问题需要解决。首先，基于 AE 的模型可以有效地重建背景样本 (BS) 而异常则不能的假设，由于其强大的特征提取能力，在实践中可能并不总是正确的。其次，基于AE的模型主要关注样本重建的质量，无论编码特征是否表示异常或背景，这不利于异常与背景的分离。为了解决上述问题，开发了一种新颖的记忆增强AE（MAAE）模型，以更好地重建背景并抑制异常重建。具体来说，针对第一个问题，设计了一种新颖的超像素引导自适应权重计算（SAWC）模块，通过考虑误差图中的上下文信息来生成自适应权重（AW），然后将AW合并到重建损失中，其中潜在的 BS 被赋予了比训练期间的异常更大的 AW。针对第二个问题，开发了一种新颖的样本归因挖掘（SAM）模块来挖掘样本归因（即探索某个样本属于背景还是异常），并利用挖掘的背景和异常样本（AS）进行训练不同的模块可以更好地分离异常和背景。 此外，还进一步设计了基于熵的稀疏寻址（ESA）模块，通过为内存模块设计可学习的稀疏寻址权重来削弱AS的重构能力。消融研究验证了所提出的 SAWC、SAM 和 ESA 的有效性。对六个高光谱图像（HSI）数据集的广泛比较实验证明了我们的方法在综合检测性能和背景抑制方面的优越性。