Super resolution mapping of trees for urban forest monitoring in Madurai city using remote sensing

Abstract

This paper proposes a super resolution mapping of trees pixel swapping method in Madurai city. Identifying and mapping the vegetation specifically trees is a significant issue in remote sensing applications where the lack of height information becomes a hard monocular recognition task. The density and shape of the trees gets affected by other man-made objects which gives rise to an erroneous recognition. The quality of recognition may be affected by various terms like resolution, visibility, sizes or scale. Predicting trees when they are partially blocked from view is also a challenging task. A common problem associated with the application of satellite images is the frequent occurrence of mixed pixels. The motivation of this work is to extract trees using pixel swapping method. Pixel-swapping algorithm is a simple and efficient technique for super resolution mapping to change the spatial arrangement of sub-pixels in such a way that the spatial correlation between neighboring sub-pixels would be maximized. Soft classification techniques were introduced to avoid the loss of information by assigning a pixel to multiple land-use/land-cover classes according to the area represented within the pixel. This soft classification technique generates a number of fractional images equal to the number of classes. Super resolution mapping was then used to know where each class is located within the pixel, in order to obtain detailed spatial patterns. The aim of supper resolution mapping is to determine a fine resolution map of the trees from the soft classification result. The experiment is conducted with images of Madurai city obtained from WorldView2 satellite. The accuracy of the pixel swapping algorithm was 98.74%.