An Improved Approach to Change Detection in Multitemporal Remote-Sensing Images

2011 ◽  
Vol 30 (11) ◽  
pp. 2737-2741 ◽  
Author(s):  
Qi Yuan ◽  
Rong-chun Zhao
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Images ◽  
Multitemporal Remote Sensing

scholarly journals Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Liang Huang ◽  
Qiuzhi Peng ◽  
Xueqin Yu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Detection Method ◽  
Saliency Detection ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Saliency Maps ◽  
Multitemporal Remote Sensing

In order to improve the change detection accuracy of multitemporal high spatial resolution remote-sensing (HSRRS) images, a change detection method of multitemporal remote-sensing images based on saliency detection and spatial intuitionistic fuzzy C-means (SIFCM) clustering is proposed. Firstly, the cluster-based saliency cue method is used to obtain the saliency maps of two temporal remote-sensing images; then, the saliency difference is obtained by subtracting the saliency maps of two temporal remote-sensing images; finally, the SIFCM clustering algorithm is used to classify the saliency difference image to obtain the change regions and unchange regions. Two data sets of multitemporal high spatial resolution remote-sensing images are selected as the experimental data. The detection accuracy of the proposed method is 96.17% and 97.89%. The results show that the proposed method is a feasible and better performance multitemporal remote-sensing image change detection method.

Unsupervised Change Detection in Multitemporal Remote Sensing Images

2021 ◽  
pp. 1-34
Author(s):  
Sicong Liu ◽  
Francesca Bovolo ◽  
Lorenzo Bruzzone ◽  
Qian du ◽  
Xiaohua Tong
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Images ◽  
Multitemporal Remote Sensing

scholarly journals Automatic Change Detection Method of Multitemporal Remote Sensing Images Based on 2D-Otsu Algorithm Improved by Firefly Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Liang Huang ◽  
Yuanmin Fang ◽  
Xiaoqing Zuo ◽  
Xueqin Yu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Firefly Algorithm ◽  
Detection Method ◽  
Binary Classification ◽  
Remote Sensing Images ◽  
Threshold Segmentation ◽  
The Difference ◽  
Multitemporal Remote Sensing ◽  
Difference Images

This paper presents a new automatic change detection method of multitemporal remote sensing images based on 2D-Otsu algorithm improved by Firefly algorithm. The proposed method is designed to automatically extract the changing area between two temporal remote sensing images. First, two different temporal remote sensing images were acquired through difference value method of remote sensing images; then, the 2D-Otsu threshold segmentation principles are analyzed and the optimal threshold of 2D-Otsu threshold segmentation method is figured out by using the Firefly algorithm, where the difference images are conducted with binary classification to obtain the changing category and the nonchanging category; finally, the proposed method is used to carry out change detection experiments on the two selected areas, where a variety of methods are compared. Experimental results show that the proposed method can effectively and quickly extract the changing area between the two temporal remote sensing images; thus, it is an effective method of change detection for remote sensing images.

scholarly journals OBJECT-ORIENTED CHANGE DETECTION BASED ON SPATIOTEMPORAL RELATIONSHIP IN MULTITEMPORAL REMOTE-SENSING IMAGES

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1241-1248
Author(s):  
L. Liang ◽  
G. Ying ◽  
X. Wen ◽  
Y. Zhang
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Contextual Information ◽  
Object Oriented ◽  
Estimation Procedure ◽  
Likelihood Method ◽  
Remote Sensing Images ◽  
Detection Approach ◽  
Conditional Mode ◽  
Multitemporal Remote Sensing

In this paper a novel object-oriented change detection approach in multitemporal remote-sensing images is proposed. In order to improve post classification comparison (PCC) performance, we propose to exploit spatiotemporal relationship between two images acquired at two different times. The probabilities of class transitions are used to describe the temporal dependence information, while the Markov Random Field (MRF) model is utilized to represent the spatial-contextual information. Training sets are required to get initial classification results b maximum likelihood method (ML). Then an estimation procedure: iterated conditional mode (ICM) is present to revise the classification results. Change detection (change/no change) and change type recognitions (from-to types of change) are achieved by compare classification maps acquired at two different times. Experimental results on two QuickBird images confirm that the proposee method can provide higher accuracy than the PCC method, which ignores spatiotemporal relationship between two images.

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