Geographic Object-based Image Analysis for Developing Cryospheric Surface Mapping Application using Remotely Sensed High-Resolution Satellite Imagery

Abstract:

A novel semi-automated method was devised by coupling spectral index ratios (SIRs) and geographic object-based image analysis (GEOBIA) to extract cryospheric geoinformation from very high resolution WorldView 2 (WV-2) satellite imagery. The present study addresses development of multiple rule sets for GEOBIA-based classification of WV-2 imagery to accurately extract land cover features in the Larsemann Hills, Antarctica. Multi-level segmentation process was applied to WV-2 image to generate different sizes of geographic image objects corresponding to various land cover features w.r.t scale parameter. Several SIRs were applied to geographic objects at different segmentation levels to classify landmass, man-made features, snow/ice, and water bodies. A specific attention was paid to water body class to identify water areas at the image level, considering their uneven appearance on landmass and ice. The results illustrated that synergetic usage of SIRs and GEOBIA can provide accurate means to identify land cover classes with an overall classification accuracy of ≈97%. In conclusion, the results suggest that GEOBIA is a powerful tool for carrying out automatic and semiautomatic analysis for most cryospheric remote-sensing applications, and the synergetic coupling with pixel-based SIRs is found to be a superior method for mining geoinformation.