Target object detection using chicken social‐based deep belief network with hyperspectral imagery

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.