An efficient multilevel thresholding based satellite image segmentation approach using a new adaptive cuckoo search algorithm

It is the most challenging and difficult task to segment a satellite image because of its complete randomness, multiple regions of interest, weak correlation with pixels, and regions of ambiguity. There are several Nature-inspired algorithms available, which are used to overcome these difficulties and those are more efficient to generate the best threshold value for the segmentation of satellite images. Though various modern methodologies opt for better results but methods have some drawbacks too like techniques are computationally expensive and time-consuming. In this paper, we have proposed a more effective satellite image segmentation approach using a new adaptive cuckoo search (ACS) algorithm. The result obtained from the projected technique is compared with CSMcCulloch incorporating McCulloch's method for levy flight generation in Cuckoo Search (CS) algorithm by using two different objective functions namely Otsu's method and Tsallis entropy function. The measurement techniques such as PSNR, MSE, FSIM, SSIM, UIQI, and computational time in term of CPU running time have been considered for validating and evaluating the proposed method. This proposed algorithm technique has resulted in improve segmentation quality of satellite images and reduced computational time. The analysis of the convergence rate proves that ACS is superior to the CSMcCulloch algorithm for reaching the global convergence rate. These experimental outcomes help to encourage researchers in different domains such as computer vision, application of medical image analysis, machine learning as well as deep learning.

Automated summary

Segmenting satellite images is a challenging task due to their random nature, multiple regions of interest, and weak correlation with pixels. Nature-inspired algorithms can be used to improve segmentation quality, with the adaptive cuckoo search (ACS) algorithm showing superior global convergence compared to other methods. The proposed method offers a more effective approach for image segmentation, showcasing improved results and reduced computational time.