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Salient object detection is a significant task that forms the basis for many image processing and computer vision applications. In recent years, the research in this area is being extended beyond RGB images to applications involving multispectral and hyperspectral images. But most of the existing algorithms for salient object detection often yield an incomplete representation of the object and often produce saliency maps with blurred edges. In this paper, we propose an efficient hyperspectral image salient object detection method through anomaly detection by combining deep learning autoencoders with one-class support vector machines. Here, the saliency detection problem in hyperspectral images is formulated as an unsupervised deep background spectral reconstruction-based anomaly detection. Our proposed method first employs deep autoencoders to model the background of an input hyperspectral image in terms of spectral reconstruction residuals of the autoencoders and then detect the salient objects from the image through a one-class support vector machine-based anomaly detection. The proposed method was evaluated on a publicly available hyperspectral image dataset for salient object detection. The experimental results show that our proposed method is found to be more efficient and superior over other previous methods in terms of various performance measures.

Salient object detection in hyperspectral images using deep background reconstruction based anomaly detection