Dr. Xing Zhang Profile

Dr. Xing Zhang

at National Univ of Defense Technology

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Proceedings Article | 15 October 2015 Paper

A hyperspectral imagery anomaly detection algorithm based on local three-dimensional orthogonal subspace projection

Xing Zhang, Gongjian Wen

Proceedings Volume 9643, 96431W (2015) https://doi.org/10.1117/12.2194554

KEYWORDS: Detection and tracking algorithms, Ions, Hyperspectral imaging, Data modeling, 3D image processing, Image analysis, Sensors, Image processing, Target detection, Matrices

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Anomaly detection (AD) becomes increasingly important in hyperspectral imagery analysis with many practical applications. Local orthogonal subspace projection (LOSP) detector is a popular anomaly detector which exploits local endmembers/eigenvectors around the pixel under test (PUT) to construct background subspace. However, this subspace only takes advantage of the spectral information, but the spatial correlat ion of the background clutter is neglected, which leads to the anomaly detection result sensitive to the accuracy of the estimated subspace. In this paper, a local three dimensional orthogonal subspace projection (3D-LOSP) algorithm is proposed. Firstly, under the jointly use of both spectral and spatial information, three directional background subspaces are created along the image height direction, the image width direction and the spectral direction, respectively. Then, the three corresponding orthogonal subspaces are calculated. After that, each vector along three direction of the local cube is projected onto the corresponding orthogonal subspace. Finally, a composite score is given through the three direction operators. In 3D-LOSP, the anomalies are redefined as the target not only spectrally different to the background, but also spatially distinct. Thanks to the addition of the spatial information, the robustness of the anomaly detection result has been improved greatly by the proposed 3D-LOSP algorithm. It is noteworthy that the proposed algorithm is an expansion of LOSP and this ideology can inspire many other spectral-based anomaly detection methods. Experiments with real hyperspectral images have proved the stability of the detection result.

Proceedings Article | 23 October 2014 Paper

Small target detection based on three-dimensional principal component analysis in hyperspectral imagery

Xing Zhang, Gongjian Wen

Proceedings Volume 9244, 92441F (2014) https://doi.org/10.1117/12.2067118

KEYWORDS: Target detection, Matrices, Principal component analysis, Detection and tracking algorithms, 3D acquisition, Hyperspectral imaging, Sensors, 3D modeling, 3D image processing, Hyperspectral target detection

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Research on target detection in hyperspectral imagery (HSI) has drawn much attention recently in many areas. Due to the limitation of the HSI sensor’s spatial resolution, the target of interest normally occupies only a few pixels, sometimes are even present as subpixels. This may increase the difficulties in target detection. Moreover, in some cases, such as in the rescue and surveillance tasks, small targets are the most significant information. Therefore, it is very difficult but important to effectively detect the interested small target. Using a three-dimensional tensor to model an HSI data cube can preserve as many as possible the original spatial-spectral constraint structures, which is conducive to utilize the whole information for small target detection. This paper proposes a novel and effective algorithm for small target detection in HSI based on three-dimensional principal component analysis (3D-PCA). According to the 3D-PCA, the significant components usually contain most information of imagery, in contrast, the details of small targets exist in the insignificant components. So, after 3D-PCA implemented on the HSI, the significant components which indicate the background of HSI are removed and the insignificant components are used to detect small targets. The algorithm is outstanding thanks to the tensor-based method which is applied to process the HSI directly, making full use of spatial and spectral information, by employing multilinear algebra. Experiments with a real HSI show that the detection probability of interested small targets improved greatly compared to the classical RX detector.

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