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Unsupervised change detection of remotely sensed images from rural areas based on using the hybrid of improved Thresholding techniques and particle swarm optimization

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Abstract

Change Detection from remote sensing images is one of the most challenging and controversial topics among researchers in this field. Given the variety of types of surveyed areas and the data used, it cannot be claimed that there is a comprehensive way to identify different types of remote sensing data.This research aims to represent three robust and novel cost functions based on improving conventional thresholding techniques (i.e., Otsu, Fisher, and Kittler & Illingworth) combined with Particle Swarm Optimization method. The aim of the research is to detect the local-global variation of bi-temporal remotely sensed images based on images statistics but without any a-prior assumptions. The proposed method consists of four fundamental steps. The first step is a preprocessing step, as an indispensable step, in order to equalize the spectral domains of inputs images and also the geometric correction of them. The second step is a process of difference image generation to highlight the changed regions in the study areas. Then, the generated difference image is split to smaller size non-overlapped blocks with the aim of local change analysis. The third step emphasizes the importance of the proposed method and also discusses in improving the cost functions of conventional thresholding techniques as local-global techniques in detail. Next, the final change map is created based on the maximum vote method and image bands combination results. At the final step, accuracy assessment and sensitivity analysis were applied to evaluate the performance of the proposed method. Experimental result on Landsat data (i.e., Alaska region and Uremia Lake) demonstrated the accuracy improvement of three proposed change detection methods about (2% -11%) over different thresholding cost functions. Therefore, the results prove the efficiency and robustness of the proposed cost function compared to the initial cost function.

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Authors and Affiliations

  1. Department of photogrammetry and remote sensing Engineering, K.N. Toosi University of Technology, Tehran, Iran

    Sara Khanbani, Ali Mohammadzadeh & Milad Janalipour

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  1. Sara Khanbani
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  2. Ali Mohammadzadeh
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  3. Milad Janalipour
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Correspondence to Ali Mohammadzadeh.

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Communicated by: H. Babaie

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Khanbani, S., Mohammadzadeh, A. & Janalipour, M. Unsupervised change detection of remotely sensed images from rural areas based on using the hybrid of improved Thresholding techniques and particle swarm optimization. Earth Sci Inform 13, 681–694 (2020). https://doi.org/10.1007/s12145-020-00455-8

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