The Multiscale Change Profile: A Statistical Similarity Measure for Change Detection in Multitemporal SAR Images

2006 ◽  
Author(s):  
J. Inglada ◽  
G. Mercier
Keyword(s):  
Change Detection ◽  
Similarity Measure ◽  
Sar Images ◽  
Statistical Similarity

scholarly journals Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Mumu Aktar ◽  
Md. Al Mamun ◽  
Md. Ali Hossain
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Similarity Measure ◽  
Sliding Window ◽  
Remote Sensing Images ◽  
Normalized Mutual Information ◽  
Statistical Similarity ◽  
Multitemporal Remote Sensing ◽  
Sparse Samples ◽  
Window Approach

Change detection (CD) of any surface using multitemporal remote sensing images is an important research topic since up-to-date information about earth surface is of great value. Abrupt changes are occurring in different earth surfaces due to natural disasters or man-made activities which cause damage to that place. Therefore, it is necessary to observe the changes for taking necessary steps to recover the subsequent damage. This paper is concerned with this issue and analyzes statistical similarity measure to perform CD using remote sensing images of the same scene taken at two different dates. A variation of normalized mutual information (NMI) as a similarity measure has been developed here using sliding window of different sizes. In sliding window approach, pixels’ local neighborhood plays a significant role in computing the similarity compared to the whole image. Thus the insignificant global characteristics containing noise and sparse samples can be avoided when evaluating the probability density function. Therefore, NMI with different window sizes is proposed here to identify changes using multitemporal data. Experiments have been carried out using two separate multitemporal remote sensing images captured one year apart and one month apart, respectively. Experimental analysis reveals that the proposed technique can detect up to 97.71% of changes which outperforms the traditional approaches.

River ice monitoring and change detection with multi-spectral and SAR images: application over yellow river

2021 ◽  
Author(s):  
Xiuwei Zhang ◽  
Yuanzeng Yue ◽  
Lin Han ◽  
Fei Li ◽  
Xiuzhong Yuan ◽  
...  
Keyword(s):  
Change Detection ◽  
Yellow River ◽  
River Ice ◽  
Sar Images

scholarly journals Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

2021 ◽  
Vol 13 (7) ◽  
pp. 1236
Author(s):  
Yuanjun Shu ◽  
Wei Li ◽  
Menglong Yang ◽  
Peng Cheng ◽  
Songchen Han
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Change Detection ◽  
Detection Method ◽  
Training Data ◽  
Synthetic Aperture ◽  
Learning Method ◽  
Sar Images ◽  
Two Stage ◽  
Second Stage

Convolutional neural networks (CNNs) have been widely used in change detection of synthetic aperture radar (SAR) images and have been proven to have better precision than traditional methods. A two-stage patch-based deep learning method with a label updating strategy is proposed in this paper. The initial label and mask are generated at the pre-classification stage. Then a two-stage updating strategy is applied to gradually recover changed areas. At the first stage, diversity of training data is gradually restored. The output of the designed CNN network is further processed to generate a new label and a new mask for the following learning iteration. As the diversity of data is ensured after the first stage, pixels within uncertain areas can be easily classified at the second stage. Experiment results on several representative datasets show the effectiveness of our proposed method compared with several existing competitive methods.

scholarly journals Change detection in SAR images based on iterative Otsu

2020 ◽  
Vol 53 (1) ◽  
pp. 331-339
Author(s):  
Shuwen Xu ◽  
Yan Liao ◽  
Xueying Yan ◽  
Gang Zhang
Keyword(s):  
Change Detection ◽  
Sar Images
Sign in / Sign up

Export Citation Format

Share Document