Target object detection using chicken social‐based deep belief network with hyperspectral imagery. Issue 15 (16th February 2021)

• Record Type: Journal Article
• Authors: Shibi, Sherin; Rajagopal, Gayathri
• Is Part Of: IET image processing. Volume 14:Issue 15(2020)
• Page Start: 3781
• Page End: 3790
• Publication Date: 2021-02-16
• Subjects: optimisation -- learning (artificial intelligence) -- object detection -- belief networks -- image resolution -- hyperspectral imaging; chicken swarm optimisation -- social ski‐driver algorithm -- CS‐based DBN -- chicken social‐based deep belief network -- high‐resolution HSI -- automatic target object detection framework -- target object detection methods; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0344
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Target object detection is an important research direction in the area of hyperspectral imaging (HSI) as it aims to detect the anomalies or objects in HSI. Some of the existing target object detection methods are merely suitable for HSI with low resolution as they failed to apply directly in the high‐resolution HSI. Therefore, an effective target detection method named chicken social‐based deep belief network (CS‐based DBN) is proposed to achieve an automatic target object detection framework in the high‐resolution HSI. The proposed CS‐based DBN is developed by integrating the chicken swarm optimisation with the social ski‐driver algorithm. The optimal solution for detecting the target object is revealed through the fitness function, which accepts the minimal error value as the best solution. Moreover, the weights of the DBN classifier are optimally trained based on the proposed algorithm to render an accurate and optimal solution in detecting the target objects. The proposed CS‐based DBN obtained better performance through the facility of stochastic exploration in search space. Moreover, the results achieved using the proposed model in terms of accuracy, specificity, and sensitivity are 0.8950, 0.8940, and 0.9, respectively.