Abstract
The research on hyperspectral anomaly detection algorithms has become a hotspot, driven by a lot of practical applications, such as mineral exploration, environmental monitoring and the national defense force. However, most existing hyperspectral anomaly detectors are designed with a single pixel as unit, which may not make full use of the spatial and spectral information in the hyperspectral image to detect anomalies. In this paper, to fully combine and utilize the spatial and spectral information of hyperspectral images, we propose a novel spectral-based selective searching method for hyperspectral anomaly detection, which firstly combines adjacent pixels with the same spectral characteristics into regions with adaptive shape and size and then treats those regions as one processing unit. Then, by fusing adjacent regions with similar spectral characteristics, the anomaly can be successfully distinguished from background. Two standard hyperspectral datasets are introduced to verify the feasibility and effectiveness of the proposed method. The detection performance is depicted by intuitive detection images, receiver operating characteristic curves and area under curve values. Comparing the results of the proposed method with five popular and state-of-the-art methods proves that the spectral-based selective searching method is an accurate and effective method to detect anomalies.
Highlights
Hyperspectral imaging (HSI) endowed with abundant spectral information is a powerful tool for target detection in remote sensing because a variety of ground objects, such as various natural land covers and artificialities, have distinct spectral signatures [1,2,3,4]
In Ref. [40], the various local spatial distribution information of the neighboring pixels of a test pixel were considered by adding a summation strategy to the local window in the collaborative representation-based detector (CRD) algorithm, which improved the accuracy of linear representation and the detection performance, but this came at the cost of increased computational complexity and time, called local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW)
The adaptive regions composed of adjacent pixels with the same spectral characteristics are referred to as the processing unit rather than a single pixel
Summary
Hyperspectral imaging (HSI) endowed with abundant spectral information is a powerful tool for target detection in remote sensing because a variety of ground objects, such as various natural land covers and artificialities, have distinct spectral signatures [1,2,3,4]. [40], the various local spatial distribution information of the neighboring pixels of a test pixel were considered by adding a summation strategy to the local window in the CRD algorithm, which improved the accuracy of linear representation and the detection performance, but this came at the cost of increased computational complexity and time, called local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW). Anomalies are the regions that are distinguished from the background of the HSIs. Thanks to the above spectral-based selective search process, the proposed algorithm is effective and handy without assuming a statistical background distribution and constructing a background or target dictionary. The information entropy of the spectral difference curve is combined with the correlation coefficient and the Lance distance to form a dynamic similarity operator to realize accurate initial segmentation of hyperspectral images
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