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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery
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Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery

机译：转移深度学习用于高光谱图像中的异常检测

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摘要

In this letter, a novel anomaly detection framework with transferred deep convolutional neural network (CNN) is proposed. The framework is designed by considering the following facts: 1) a reference data with labeled samples are utilized, because no prior information is available about the image scene for anomaly detection and 2) pixel pairs are generated to enlarge the sample size, since the advantage of CNN can be realized only if the number of training samples is sufficient. A multilayer CNN is trained by using difference between pixel pairs generated from the reference image scene. Then, for each pixel in the image for anomaly detection, difference between pixel pairs, constructed by combining the center pixel and its surrounding pixels, is classified by the trained CNN with the result of similarity measurement. The detection output is simply generated by averaging these similarity scores. Experimental performance demonstrates that the proposed algorithm outperforms the classic Reed-Xiaoli and the state-of-the-art representation-based detectors, such as sparse representation-based detector (SRD) and collaborative representation-based detector.
机译：在这封信中，提出了一种新的带有深度卷积神经网络（CNN）的异常检测框架。通过考虑以下事实来设计该框架：1）使用带有标记样本的参考数据，因为没有关于图像场景的先验信息可用于异常检测，并且2）生成像素对以扩大样本大小，因为其优点仅当训练样本数量足够时，才能实现CNN编码。通过使用从参考图像场景生成的像素对之间的差异来训练多层CNN。然后，对于用于异常检测的图像中的每个像素，通过训练的CNN将通过组合中心像素及其周围像素而构造的像素对之间的差异与相似性测量结果进行分类。通过平均这些相似度分数即可简单地生成检测输出。实验性能表明，所提出的算法优于经典的Reed-Xiaoli和基于状态的基于表示的检测器，例如基于稀疏表示的检测器（SRD）和基于协作表示的检测器。

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