Intelligent adaptive resampling technique for the processing of remotely sensed imagery

Abstract

Resampling techniques are being widely used at different stages of satellite image processing. The existing methodologies cannot perfectly recover features from a completely undersampled image and hence an intelligent adaptive resampling methodology is required. We address these issues and adopt an error metric from the available literature to define interpolation quality. We also propose a new resampling scheme that adapts itself with regard to the pixel and texture variation in the image. The proposed cellular neural network (CNN)-based hybrid method has been found to outperform existing methods as it adapts itself with reference to the change in sub pixel variation.

Remote Sensing Assessment of Forest Disturbance across Complex Mountainous Terrain: The Pattern and Severity of Impacts of Tropical Cyclone Yasi on Australian Rainforests

“…In this study we used the nearest neighbor approach since it appears to yield good results [53] but, as observed, this approach produced underestimates in the coarser resolution. Optimum scaling methodology is an area of current study [54], but ultimately any process is expected to reduce the proportion and value of ΔNPV [55][56][57], thereby under-estimating overall tree mortality.…”