A super-resolution mapping algorithm based on the level set method

The presence of mixed pixels is a recurring problem in extracting accurate land cover information from remote sensing images. To deal with the mixed-pixel problem, we propose to find the land cover map at the resolution higher than the observed remote sensing image. The process to obtain this higher resolution land cover map is called “super resolution land cover mapping (SRLCM).” In this work, we modeled the problem of the SRLCM as an image segmentation problem where the level set method can be applied to find the boarders between land cover classes at the sub-pixel accuracy. Our experimental results show that our proposed approach can significantly improve the classification accuracy over the Maximum likelihood classifier.

[1]  Giles M. Foody,et al.  Combining Pixel Swapping and Contouring Methods to Enhance Super-Resolution Mapping , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[3]  Pramod K. Varshney,et al.  Distributed Detection and Data Fusion , 1996 .

[4]  Ye Zhang,et al.  Integration of Spatial–Spectral Information for Resolution Enhancement in Hyperspectral Images , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Thomas L. Marzetta,et al.  Detection, Estimation, and Modulation Theory , 1976 .

[6]  Andrew J. Tatem.  Super-resolution land cover mapping from remotely sensed imagery using a Hopfield neural network , 2001 .

[7]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[8]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[9]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[10]  Giles M. Foody,et al.  Combining Hopfield Neural Network and Contouring Methods to Enhance Super-Resolution Mapping , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Peter M. Atkinson.  Super-Resolution Land Cover Classification Using the Two-Point Histogram , 2004 .

[12]  H. V. Trees.  Detection, Estimation, And Modulation Theory , 2001 .

[13]  Robert De Wulf,et al.  Land cover mapping at sub-pixel scales using linear optimization techniques , 2002 .

[14]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).