Weighted Support Vector Machine Segmentation of SAR Images Based on MARMA model

2007 ◽  
Author(s):  
Peng-wei Wang ◽  
Xiu-qing Wu ◽  
Shan Yu
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Sar Images

Change detection of SAR images using morphologic attribute profile and support vector machine

2014 ◽  
Vol 22 (10) ◽  
pp. 2832-2839 ◽  
Author(s):  
张雄美 ZHANG Xiong-mei ◽  
易昭湘 YI Zhao-xiang ◽  
田淞 TIAN Song ◽  
宋建社 SONG Jian-she
Keyword(s):  
Support Vector Machine ◽  
Change Detection ◽  
Support Vector ◽  
Sar Images

scholarly journals CLASSIFICATION OF MULTI-TEMPORAL SAR IMAGES FOR RICE CROPS USING COMBINED ENTROPY DECOMPOSITION AND SUPPORT VECTOR MACHINE TECHNIQUE

2007 ◽  
Vol 71 ◽  
pp. 19-39 ◽  
Author(s):  
Chue-Poh Tan ◽  
Jun-Yi Koay ◽  
Ka-Sing Lim ◽  
Hong-Tat Ewe ◽  
Hean-Teik Chuah
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Sar Images ◽  
Multi Temporal

Change Detection in SAR Images Based on Semi-Supervised Learning

2014 ◽  
Vol 596 ◽  
pp. 484-489
Author(s):  
Song Tian ◽  
Jian She Song ◽  
Shu Bing Tian ◽  
Wei Gong
Keyword(s):  
Support Vector Machine ◽  
Change Detection ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Detection Accuracy ◽  
Sar Image ◽  
Training Set ◽  
Sar Images ◽  
Large Numbers ◽  
Vector Machines

Support Vector Machine (SVM) is a supervised approach, which needs large numbers of labeled samples. However, it is difficult to obtain such samples for change detection based on SAR images and the available labeled samples are very limited. this paper proposes a semi-supervised support vector machine (S3VM) unsupervised SAR image change detection. Using of K-means clustering method obtain threshold of image; introduce offsets which are automatically selected to achieve a pseudo-training set and unlabeled set; Finally, based on the statistical characteristics of semi-supervised support vector machines for image change and non-change class. The experimental results showed that: In the case without using noise reduction and the reduction in the number of samples, the proposed algorithm can maintain better classification, generalization and more stable detection accuracy.

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