Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes (original) (raw)
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International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/an-approach-for-change-detection-in-multispectral-remotely-sensed-data-using-interactive-segmentation https://www.ijert.org/research/an-approach-for-change-detection-in-multispectral-remotely-sensed-data-using-interactive-segmentation-IJERTV3IS052116.pdf to solve the change detection(CD) problem in multitemporal remote-sensing images using interactivesegmentation methods. The user needs to input markersrelated to change and no-change classes in the difference image.Then, the pixels under these markers are used by the supportvector machine classifier to generate a spectral-change map.The most common methodology to carry out automatic unsupervised change detection in remotely sensed imageryis to find the best global threshold in the histogram of the so-calleddifference image.The user first roughly scribblesdifferent regions of interest, and from them, the whole imageis automatically segmented. Toenhance further the result, we include the spatial contextual informationin the decision process using two different solutionsbased on Markov random field and level-set methods. While theformer is a region-driven method, the latter exploits both regionand contour for performing the segmentation task. Experimentsconducted on a set of four real remote-sensing images acquired bylow as well as very high spatial resolution sensors and referring todifferent kinds of changes confirm the attractive capabilities of theproposed methods in generating accurate CD maps with simpleand minimal interaction.referring to different kinds ofchanges show the high robustness of the proposed unsupervisedchange detection approach. .
IJERT-A Novel Approach for Change Detection in Multispectral Remotely Sensed Data
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/a-novel-approach-for-change-detection-in-multispectral-remotely-sensed-data https://www.ijert.org/research/a-novel-approach-for-change-detection-in-multispectral-remotely-sensed-data-IJERTV3IS040396.pdf In this letter, we propose to solve the change detection (CD) problem in multitemporal remote-sensing images using interactive segmentation methods. The user needs to input markers related to change and no-change classes in the difference image. Then, the pixels under these markers are used by the support vector machine (SVM) classifier to generate a spectral-change map. To enhance further the result, we include the spatial contextual information in the decision process using two different solutions based on Markov random field and level-set methods. This paper presents an interactive algorithm for segmentation of natural images about an area, city, vegetation field etc,.
Change detection using multispectral satellite images: a systematic review of literature
Bulletin of Electrical Engineering and Informatics, 2024
Change detection (CD) provides information about the changes on earth's surface over a period of time. Many algorithms have been proposed over the years for effective CD of satellite imagery. This paper presents the steps to preprocess the captured satellite images, which can be used to perform predictive analysis of earth's surface by CD techniques. To design a highly effective system for CD, it is recommended that algorithm designers select efficient algorithms from any given application. Thus, this paper presents and investigates the review of most appropriate literature on CD, where CD techniques have been presented into three groups; i) thresholding, ii) clustering, and iii) deep learning. The first two categories mainly rely on the traditional machine learning, whereas the last one exploits the state-of-theart deep learning models. At the end, the standard methods are summarized based on advantages, limitation, and research gap.