Object-oriented classification of polarimetric SAR imagery based on Statistical Region Merging and Support Vector Machine

2008 ◽  
Author(s):  
H. T. Li ◽  
H.Y. Gu ◽  
Y. S. Han ◽  
J. H. Yang
Keyword(s):  
Support Vector Machine ◽  
Object Oriented ◽  
Support Vector ◽  
Region Merging ◽  
Polarimetric Sar ◽  
Sar Imagery ◽  
Statistical Region Merging

scholarly journals MULTISCALE SEGMENTATION OF POLARIMETRIC SAR IMAGE BASED ON SRM SUPERPIXELS

2016 ◽  
Vol XLI-B7 ◽  
pp. 283-287
Author(s):  
F. Lang ◽  
J. Yang ◽  
L. Wu ◽  
D. Li
Keyword(s):  
Object Oriented ◽  
Test Site ◽  
Region Merging ◽  
Polarimetric Sar ◽  
Multi Scale ◽  
Single Scale ◽  
Binary Partition ◽  
Segmentation Algorithms ◽  
Merging Process ◽  
Statistical Region Merging

Multi-scale segmentation of remote sensing image is more systematic and more convenient for the object-oriented image analysis compared to single-scale segmentation. However, the existing pixel-based polarimetric SAR (PolSAR) image multi-scale segmentation algorithms are usually inefficient and impractical. In this paper, we proposed a superpixel-based binary partition tree (BPT) segmentation algorithm by combining the generalized statistical region merging (GSRM) algorithm and the BPT algorithm. First, superpixels are obtained by setting a maximum region number threshold to GSRM. Then, the region merging process of the BPT algorithm is implemented based on superpixels but not pixels. The proposed algorithm inherits the advantages of both GSRM and BPT. The operation efficiency is obviously improved compared to the pixel-based BPT segmentation. Experiments using the Lband ESAR image over the Oberpfaffenhofen test site proved the effectiveness of the proposed method.

scholarly journals MULTISCALE SEGMENTATION OF POLARIMETRIC SAR IMAGE BASED ON SRM SUPERPIXELS

2016 ◽  
Vol XLI-B7 ◽  
pp. 283-287
Author(s):  
F. Lang ◽  
J. Yang ◽  
L. Wu ◽  
D. Li
Keyword(s):  
Object Oriented ◽  
Test Site ◽  
Region Merging ◽  
Polarimetric Sar ◽  
Multi Scale ◽  
Single Scale ◽  
Binary Partition ◽  
Segmentation Algorithms ◽  
Merging Process ◽  
Statistical Region Merging

Multi-scale segmentation of remote sensing image is more systematic and more convenient for the object-oriented image analysis compared to single-scale segmentation. However, the existing pixel-based polarimetric SAR (PolSAR) image multi-scale segmentation algorithms are usually inefficient and impractical. In this paper, we proposed a superpixel-based binary partition tree (BPT) segmentation algorithm by combining the generalized statistical region merging (GSRM) algorithm and the BPT algorithm. First, superpixels are obtained by setting a maximum region number threshold to GSRM. Then, the region merging process of the BPT algorithm is implemented based on superpixels but not pixels. The proposed algorithm inherits the advantages of both GSRM and BPT. The operation efficiency is obviously improved compared to the pixel-based BPT segmentation. Experiments using the Lband ESAR image over the Oberpfaffenhofen test site proved the effectiveness of the proposed method.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference
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