Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Classification of Fused SAR/EO Images Using Transformation of Fusion Classification Class Label

  • Ye, Chul-Soo (Department of Ubiquitous IT, Far East University)
  • Received : 2012.11.09
  • Accepted : 2012.12.17
  • Published : 2012.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Strong backscattering features from high-resolution Synthetic Aperture Rader (SAR) image provide useful information to analyze earth surface characteristics such as man-made objects in urban areas. The SAR image has, however, some limitations on description of detail information in urban areas compared to optical images. In this paper, we propose a new classification method using a fused SAR and Electro-Optical (EO) image, which provides more informative classification result than that of a single-sensor SAR image classification. The experimental results showed that the proposed method achieved successful results in combination of the SAR image classification and EO image characteristics.

Keywords

References

  1. Fatone, L., P. Maponi, and F. Zirilli, 2001. Fusion of SAR/optical images to detect urban areas, Remote Sensing and Data Fusion over Urban Areas, IEEE/ISPRS Joint Workshop 2001, 217-221.
  2. Garzelli, A., 2002. Wavelet-based fusion of optical and SAR image data over urban area, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 34(3): 59-62.
  3. Li, Z., Z. Jing, G. Liu, S. Sun, and H. Leung, 2003. A region-based image fusion algorithm using multiresolution segmentation, IEEE Proceedings of 2003 Intelligent Transportation Systems, 96- 101.
  4. Long, Z. and W. Guangfang, 2007. Study on adaptation of SAR/CCD image fusion algorithm, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing, Proceedings of SPIE, 6623: 66230.
  5. Petrovic, V. and C. Xydeas, 2004. Gradient based multiresolution image fusion, IEEE Transactions on Image Processing, 4(2): 163-183.
  6. Piella, G., 2003. A general framework for multiresolution image fusion: from pixels to regions, International Journal of Information Fusion, 4: 258-280.
  7. Ye, C.S., 2009. Fusing optical and SAR images using wavelet transform and multiscale images, Proceedings of International Symposium on Remote Sensing 2009, Busan, Korea, Oct. 28-30, 520-523.
  8. Ye, C.S., 2010. Wavelet-based fusion of optical and radar image using gradient and variance, Korean Journal of Remote Sensing, 26(5): 581-591. https://doi.org/10.7780/kjrs.2010.26.5.581

Cited by

  1. Evaluating the Contribution of Spectral Features to Image Classification Using Class Separability vol.36, pp.1, 2012, https://doi.org/10.7780/kjrs.2020.36.1.5