scholarly journals An Unsupervised Method of Change Detection in Multi-Temporal PolSAR Data Using a Test Statistic and an Improved K&I Algorithm

2017 ◽  
Vol 7 (12) ◽  
pp. 1297 ◽  
Author(s):  
Jinqi Zhao ◽  
Jie Yang ◽  
Zhong Lu ◽  
Pingxiang Li ◽  
Wensong Liu ◽  
...  
Keyword(s):  
Change Detection ◽  
Test Statistic ◽  
Unsupervised Method ◽  
Multi Temporal

scholarly journals A Novel Change Detection Method Based on Statistical Distribution Characteristics Using Multi-Temporal PolSAR Data

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1508 ◽  
Author(s):  
Jinqi Zhao ◽  
Yonglei Chang ◽  
Jie Yang ◽  
Yufen Niu ◽  
Zhong Lu ◽  
...  
Keyword(s):  
Change Detection ◽  
Optimal Threshold ◽  
Distribution Model ◽  
Test Statistic ◽  
Omnibus Test ◽  
Polarimetric Synthetic Aperture Radar ◽  
Difference Image ◽  
East Lake ◽  
Multi Temporal ◽  
The Difference

Unsupervised change detection approaches, which are relatively straightforward and easy to implement and interpret, and which require no human intervention, are widely used in change detection. Polarimetric synthetic aperture radar (PolSAR), which has an all-weather response capability with increased polarimetric information, is a key tool for change detection. However, for PolSAR data, inadequate evaluation of the difference image (DI) map makes the threshold-based algorithms incompatible with the true distribution model, which causes the change detection results to be ineffective and inaccurate. In this paper, to solve these problems, we focus on the generation of the DI map and the selection of the optimal threshold. An omnibus test statistic is used to generate the DI map from multi-temporal PolSAR images, and an improved Kittler and Illingworth algorithm based on either Weibull or gamma distribution is used to obtain the optimal threshold for generating the change detection map. Multi-temporal PolSAR data obtained by the Radarsat-2 sensor over Wuhan in China are used to verify the efficiency of the proposed method. The experimental results using our approach obtained the best performance in East Lake and Yanxi Lake regions with false alarm rates of 1.59% and 1.80%, total errors of 2.73% and 4.33%, overall accuracy of 97.27% and 95.67%, and Kappa coefficients of 0.6486 and 0.6275, respectively. Our results demonstrated that the proposed method is more suitable than the other compared methods for multi-temporal PolSAR data, and it can obtain both effective and accurate results.

scholarly journals CHANGE DETECTION OF POLARIMETRIC SAR DATA FOR MONITORING OF AGRICULTURAL AREAS

2018 ◽  
Vol IV-5 ◽  
pp. 229-232
Author(s):  
N. Varia ◽  
N. Davawala ◽  
S. Chirakkal ◽  
D. Haldar ◽  
R. Ghosh ◽  
...  
Keyword(s):  
Change Detection ◽  
Agricultural Land ◽  
Covariance Matrices ◽  
Agricultural Crop ◽  
Test Statistic ◽  
Significance Level ◽  
Changing Trends ◽  
Multi Temporal ◽  
Agricultural Areas ◽  
Change Map

<p><strong>Abstract.</strong> Change detection is important to understand the patterns of transition in multi-temporal SAR acquisitions over same geographical areas. In this work, we implemented a test statistic on covariance matrices for change detection. The RADARSAT-2 data spanning the agricultural land of Central Hisar Farm in Haryana, India was used. Hypothesis testing on test-statistic was done by a pre-decided significance level. A change map was plotted and the areas with ‘change’ and ‘no change’ were determined. Analysis of changing trends of different crop lifecycles is done. This study is useful in making important agricultural crop predictions.</p>

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 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 Novel Method of Unsupervised Change Detection Using Multi-Temporal PolSAR Images

2017 ◽  
Vol 9 (11) ◽  
pp. 1135 ◽  
Author(s):  
Wensong Liu ◽  
Jie Yang ◽  
Jinqi Zhao ◽  
Le Yang
Keyword(s):  
Change Detection ◽  
Multi Temporal ◽  
Novel Method

scholarly journals Multi-temporal Satellite Images Change Detection Algorithm Based on NSCT

2011 ◽  
Vol 24 ◽  
pp. 252-256 ◽  
Author(s):  
Wei Cui ◽  
Zhenhong Jia ◽  
Xizhong Qin ◽  
Jie Yang ◽  
Yingjie Hu
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Multi Temporal
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