Volume I-7
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 227-235, 2012
https://doi.org/10.5194/isprsannals-I-7-227-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 227-235, 2012
https://doi.org/10.5194/isprsannals-I-7-227-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  17 Jul 2012

17 Jul 2012

A CHANGE DETECTION METHOD FOR REMOTE SENSING IMAGE BASED ON MULTI-FEATURE DIFFERENCING KERNEL SVM

Y. Lin1,2, B. Liu1,2, Q.-l. Lv1,2, C. Pan3, and Y. Lu1 Y. Lin et al.
  • 1Research Center of Remote Sensing and Spatial Information Technology, Tongji University, Shanghai, 200092, China
  • 2Department of Surveying and Geoinformatics, Tongji University, Shanghai, 200092, China
  • 3Shanghai Municipal Institute of Surveying and Mapping, Shanghai, 200063, China

Keywords: Multi-Feature Differencing Kernel, Change Detection, Support Vector Machine

Abstract. Based on the support vector machine (SVM) tools and multiple kernel method, the combinations of kernel functions were mainly discussed. The construction method of image differencing kernel with multi-feature (spectral feature and textural feature) has been developed. Through this method and weighting of the categories' samples, the improved SVM change detection model has been proposed, which could realize the direct extraction of spatial distribution information from several change classes. From the experiments we can draw the following conclusions: with the help of multiple kernel function integrating spectral features and texture information, the new change detection model can achieve higher detection accuracy than the traditional methods and is suitable for the small-sample experiment. Furthermore, it avoids the complex and uncertainty in determining change threshold required in the old detection methods.