Mr. Boyang Nie Profile

Boyang Nie

at Aerospace Information Research Institute CAS

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Publications (2)

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Proceedings Article | 20 November 2024 Presentation + Paper

Hyperspectral anomaly detection method based on tensor decomposition and information entropy

Lei Yang, Jinsong Zhou, Juanjuan Jing, Boyang Nie, Yacan Li, Xiaoying He, Lei Feng

Proceedings Volume 13196, 131960N (2024) https://doi.org/10.1117/12.3031378

KEYWORDS: Hyperspectral imaging, Target detection, Image information entropy, Hyperspectral target detection, Spectral resolution, Information theory

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Hyperspectral images are constantly improving their spectral resolution and spatial resolution，so utilizing hyperspectral images for object detection has become a research hotspot in the field of hyperspectral remote sensing. Anomaly detection is the main way to achieve object detection. Abnormal targets in hyperspectral images are usually composed of a few pixels (or even sub-pixel) that are clearly different from the surrounding background pixels. Compared with the background, abnormal targets have two characteristics: spectral anomaly and spatial anomaly. Traditional hyperspectral image anomaly detection methods only utilize spectral anomalies and ignore spatial anomalies between pixels. Hyperspectral images can be represented by third-order tensors, where the first two orders of the third-order tensor are used to represent the spatial dimension of the image (i.e. the height and width of the image), and the third order is used to represent the spectral dimension. Therefore, tensor decomposition can simultaneously represent the spatial and spectral features of anomalous targets. This paper proposes a new anomaly detection method based on tensor decomposition and information entropy. This method is mainly divided into three steps. Firstly, a third-order tensor is used to represent the cube of the detected hyperspectral image, and the Tucker decomposition of the third-order tensor is applied to the detected hyperspectral image. Secondly, the background information in the detected hyperspectral image is removed using information entropy, and the remaining feature components are reconstructed into the hyperspectral image. Thirdly, the RX algorithm is used to detect anomalies in the reconstructed hyperspectral image. Compared with methods based on spectral anomalies, this method has better detection efficiency.

Proceedings Article | 10 October 2019 Paper

Design of miniaturization hyperspectral imager based on CMOS sensor

Lei Yang, Jinsong Zhou, Juanjuan Jing, Lidong Wei, Xiaoying He, Yacan Li, Lei Feng, Li Xu, Boyang Nie

Proceedings Volume 11151, 1115127 (2019) https://doi.org/10.1117/12.2533027

KEYWORDS: Imaging systems, Hyperspectral imaging, CMOS sensors, Spectroscopy, Sensors, Interfaces, Agriculture, Environmental monitoring, Telescopes, Image sensors

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Hyperspectral imager can obtain both spatial information and spectral information at the same time, which is widely used in agriculture, biomedicine, environment monitoring. At present, hyperspectral imager is developing in the direction of lightweight, miniaturization and low power consumption. The miniaturization of hyperspectral imager includes the miniaturization of optical system and the miniaturization of detector system. In this paper, a miniaturization hyperspectral imager based on CMOS detector is proposed. The working range of is 400-1000nm, which contains 512 spectral bands. The field angle of hyperspectral imager is ±4 degree and the focal length is 99mm. The optical system consists of the telescope, the slit and the spectrometer. Considering the miniaturization of optical system, the spectrometer uses prism-grating spectroscopy. The CMOS sensor GSENSE400BSI is used in the detector system, whose pixel size is 11 micron*11 micron and pixel number is 2048*2048.The detector system consists of an imaging core board and an interface board, and the image output is Cameralink interface. Because of the high integration of CMOS sensor, the design of peripheral circuit can be greatly simplified. The total weight of the hyperspectral imager is not more than 2.5kg and the total power consumption is not more than 5W.The spectral imaging system has the advantages of lightweight, miniaturization and low power consumption. After testing, the spectrometer has good imaging quality.

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