Remote Sensing Imagery Segmentation: A Hybrid Approach

Remote Sensing

Himanshu Mittal¹  Amit Saxena²  Mohammad Sajid³  Witold Pedrycz⁴  Jagdish Chand Bansal⁵  Tony Jan⁶  Mukesh Prasad⁷  Shreya Pare⁷ 

[1] Department of Computer Science Engineering and IT, Jaypee Institute of Information Technology, Noida 201309, India;Department of Computer Science and IT, Guru Ghashidash University, Bilashpur 495009, India;Department of Computer Science, Aligarh Muslim University, Aligarh 202001, India;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G2R3, Canada;Department of Mathematics, South Asian University, New Delhi 110021, India;Design and Technology Vertical, Torrens University, Sydney 2007, Australia;Faculty of Engineering and Information Technolgy, School of Computer Science, University of Technology Sydney, Sydney 2007, Australia;

关键词: image segmentation;    remote sensing images;    multilevel Rényi’s entropy;    cuckoo search;    optimization algorithms;   

DOI  :  10.3390/rs13224604

来源: DOAJ

【 摘 要 】

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.

【 授权许可】

Unknown