A Change Detection Method of Multi-temporal SAR Images Based on Contourlet Transform

2016 ◽  
Vol 6 (0) ◽  
pp. 53
Author(s):  
Shiqi Huang ◽  
Wenzhun Huang ◽  
Ting Zhang
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Contourlet Transform ◽  
Sar Images ◽  
Multi Temporal

scholarly journals AN UNSUPERVISED CHANGE DETECTION BASED ON TEST STATISTIC AND KI FROM MULTI-TEMPORAL AND FULL POLARIMETRIC SAR IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 611-615
Author(s):  
J. Q. Zhao ◽  
J. Yang ◽  
P. X. Li ◽  
M. Y. Liu ◽  
Y. M. Shi
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Speckle Noise ◽  
Natural Phenomena ◽  
Threshold Selection ◽  
Test Statistic ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Multi Temporal ◽  
Selection Of

Accurate and timely change detection of Earth’s surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Many traditional methods of change detection only use a part of polarization information and the supervised threshold selection. Those methods are insufficiency and time-costing. In this paper, we present a novel unsupervised change-detection method based on quad-polarimetric SAR data and automatic threshold selection to solve the problem of change detection. First, speckle noise is removed for the two registered SAR images. Second, the similarity measure is calculated by the test statistic, and automatic threshold selection of KI is introduced to obtain the change map. The efficiency of the proposed method is demonstrated by the quad-pol SAR images acquired by Radarsat-2 over Wuhan of China.

scholarly journals A SAR INTENSITY IMAGES CHANGE DETECTION METHOD BASED ON FUSION DIFFERENCE DETECTOR AND STATISTICAL PROPERTIES

2017 ◽  
Vol IV-2/W4 ◽  
pp. 439-443 ◽  
Author(s):  
B. Cui ◽  
Y. Zhang ◽  
L. Yan ◽  
X. Cai
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Hypothesis Test ◽  
Threshold Selection ◽  
Sar Images ◽  
Difference Map ◽  
Multi Temporal ◽  
Intensity Images ◽  
Log Ratio ◽  
Change Threshold

Detecting the land cover changes is an important application of multi-temporal synthetic aperture radar (SAR) images. This study puts forward a novel SAR change detection method which has two-steps: change detector construction and change threshold selection. For change detector construction, considering the SAR intensity images follow the gamma distribution, the conditional probabilities of the binary hypothesis test are provided, then the log likelihood ratio (LLR) combined with the log ratio (LR) to construct a detector which can enhance the degree of change to calculate the diversity degree convenient between the two images; for change threshold selection, owing to the characteristic that the curve about the ratio value of adjacent grey-level (GL) values in normalized difference map, the normalized difference map can be segmented in three parts by two thresholds selected which correspond to the regions of unchanged, backscatter enhanced and weakened separately. And as this, the change areas can be also determined simultaneously. The experimental results on different areas and sensors indicate that the proposed algorithm is effective and feasible.

A DCT-based Change Detection Method for Multi-Temporal SAR Images

2008 ◽  
Author(s):  
Hongtao Yu ◽  
Zhiping Lin ◽  
Geok Ling Chai ◽  
Chye Hwang Yan ◽  
Nalliah Raman
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Sar Images ◽  
Multi Temporal

scholarly journals AN UNSUPERVISED CHANGE DETECTION BASED ON TEST STATISTIC AND KI FROM MULTI-TEMPORAL AND FULL POLARIMETRIC SAR IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 611-615 ◽  
Author(s):  
J. Q. Zhao ◽  
J. Yang ◽  
P. X. Li ◽  
M. Y. Liu ◽  
Y. M. Shi
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Speckle Noise ◽  
Natural Phenomena ◽  
Threshold Selection ◽  
Test Statistic ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Multi Temporal ◽  
Selection Of

Accurate and timely change detection of Earth’s surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Many traditional methods of change detection only use a part of polarization information and the supervised threshold selection. Those methods are insufficiency and time-costing. In this paper, we present a novel unsupervised change-detection method based on quad-polarimetric SAR data and automatic threshold selection to solve the problem of change detection. First, speckle noise is removed for the two registered SAR images. Second, the similarity measure is calculated by the test statistic, and automatic threshold selection of KI is introduced to obtain the change map. The efficiency of the proposed method is demonstrated by the quad-pol SAR images acquired by Radarsat-2 over Wuhan of China.

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.

A Novel SAR Images Change Detection Method Based on Dynamic TUNET-CRF Model

2021 ◽  
Author(s):  
Jianlong Zhang ◽  
Mengying Cui ◽  
Bin Wang ◽  
Chen Chen ◽  
Yang Zhou ◽  
...  
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Sar Images

A variational change detection method for multitemporal SAR images

2014 ◽  
Vol 5 (4) ◽  
pp. 342-351 ◽  
Author(s):  
Yin Chen ◽  
Armin B. Cremers ◽  
Zhiguo Cao
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Sar Images

scholarly journals Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

2020 ◽  
Vol 12 (3) ◽  
pp. 548 ◽  
Author(s):  
Xinzheng Zhang ◽  
Guo Liu ◽  
Ce Zhang ◽  
Peter M. Atkinson ◽  
Xiaoheng Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Speckle Noise ◽  
Sar Image ◽  
Phase Object ◽  
Sar Images ◽  
Two Phase ◽  
Object Based ◽  
Multi Temporal ◽  
Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.

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