SRA–CEM: An Improved CEM Target Detection Algorithm for Hyperspectral Images Based on Subregion Analysis

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 16; pp. 6026 - 6037
• Main Authors: Zhao, Jiale, Wang, Guanglong, Zhou, Bing, Ying, Jiaju, Liu, Jie
• Format: Journal Article
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023; IEEE
• Subjects: Algorithms; Analysis; Constrained energy minimization (CEM) algorithm; Detection; Hyperspectral imaging; Imaging techniques; Information processing; Reflectance; Spatial analysis; Spatial discrimination; Spatial resolution; spatial–spectral combination; Spectral analysis; Spectral reflectance; Spectrum analysis; Target detection
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2023.3289943

Due to the limitations of spatial resolution and detector level, traditional hyperspectral image (HSI) target detection focuses more on spectral analysis, and spatial morphology information is not fully utilized in HSI target detection. The constrained energy minimization (CEM) method is a classic HSI target detection algorithm that can highlight the information of the target, suppress background information, and achieve the effect of separating the target from the image. However, the CEM method is a supervised algorithm that requires obtaining spectral information of the target in advance. Due to various factors, such as material composition, object shape, and imaging conditions, the spectral reflectance of targets usually exhibits strong uncertainty, which is the main reason why the detection performance of traditional target detection algorithms is not ideal. To address the above issues, an improved CEM target detection algorithm for HSIs based on subregion analysis (SRA–CEM) was proposed. The SRA–CEM method first obtains the subregion where the target is located based on its external features and then uses background detection to infer the specific location of the target. SRA–CEM uses prior background spectral reflectance to replace the spectral reflectance of unknown and variable targets and can avoid the impact of the target signal as a background signal in the traditional CEM algorithm on the detection results. Experiments were conducted using publicly available and self-test hyperspectral data, respectively. The results showed that compared to other target detection algorithms, the SRA—CEM method could effectively improve the accuracy of hyperspectral target detection. Especially in HSIs under land-based imaging conditions, the area under the curve value of the SRA–CEM method has increased by about 0.11.