For wide application of change detection with SAR imagery, current processing technologies and methods are mostly based on pixels. It is difficult for pixel-based technologies to utilize spatial characteristics of images and topological relations of objects. Object-oriented technology takes objects as processing unit, which takes advantage of the shape and texture information of image. It can greatly improve the efficiency and reliability of change detection. Recently, with the development of polarimetric synthetic aperture radar (PolSAR), more backscattering features on different polarization state can be available for usage of object-oriented change detection study. In this paper, the object-oriented strategy will be employed. Considering the fact that the different target or target's state behaves different backscattering characteristics dependent on polarization state, an object-oriented change detection method that based on weighted polarimetric scattering difference of PolSAR images is proposed. The method operates on the objects generated by generalized statistical region merging (GSRM) segmentation processing. The merit of GSRM method is that image segmentation is executed on polarimetric coherence matrix, which takes full advantages of polarimetric backscattering features. And then, the measurement of polarimetric scattering difference is constructed by combining the correlation of covariance matrix and the difference of scattering power. Through analysing the effects of the covariance matrix correlation and the scattering echo power difference on the polarimetric scattering difference, the weighted method is used to balance the influences caused by the two parts, so that more reasonable weights can be chosen to decrease the false alarm rate. The effectiveness of the algorithm that proposed in this letter is tested by detection of the growth of crops with two different temporal radarsat-2 fully PolSAR data. First, objects are produced by GSRM algorithm based on the coherent matrix in the pre-processing. Then, the corresponding patches are extracted in two temporal images to measure the differences of objects. To detect changes of patches, a difference map is created by means of weighted polarization scattering difference. Finally, the result of change detection can be obtained by threshold determining. The experiments show that this approach is feasible and effective, and a reasonable choice of weights can improve the detection accuracy significantly.
[1]
H. Skriver,et al.
Complex Wishart Distribution Based Analysis of Polarimetric Synthetic Aperture Radar Data
,
2007,
2007 International Workshop on the Analysis of Multi-temporal Remote Sensing Images.
[2]
J. Susaki,et al.
Unsupervised change detection in an urban environment using multitemporal polsar images
,
2013,
Joint Urban Remote Sensing Event 2013.
[3]
E. Pottier,et al.
Polarimetric Radar Imaging: From Basics to Applications
,
2009
.
[4]
Muhtar Qong,et al.
Polarization state conformation and its application to change detection in polarimetric SAR data
,
2004,
IEEE Geosci. Remote. Sens. Lett..
[5]
Hong Zhang,et al.
PolSAR change detection for specific land cover type by testing equality of two PolInSAR coherency matrixes
,
2012,
2012 International Conference on Computer Vision in Remote Sensing.
[6]
Xi Chen,et al.
Change Detection with Multi-Polarization SAR Imagery
,
2008,
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.
[7]
Y. Zhang,et al.
A REVIEW ON IMAGE SEGMENTATION TECHNIQUES WITH REMOTE SENSING PERSPECTIVE
,
2010
.
[8]
Shaun Quegan,et al.
Segmentation based change detection in ERS-1 SAR images
,
1994,
Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.
[9]
Zhai Liang,et al.
Object-oriented change detection of multi-polarization SAR images based on unitemporal image segmentation
,
2013,
Joint Urban Remote Sensing Event 2013.
[10]
Lei Shi,et al.
Polarimetric SAR Image Segmentation Using Statistical Region Merging
,
2014,
IEEE Geoscience and Remote Sensing Letters.