An Efficient Image Preprocessing Technique for Land Use Analysis by Using Remote Sensing Images

Abstract

The advancement in remote sensing technology expands the choice of selection of images for land use mapping. Mapping, specific land use from remotely sensed images requires significant processing steps begins with image acquisition to classification using related algorithms. Useful properties of remote sensing images inherited with variety of errors requires some set of treatment in the form of preprocessing using various preprocessing techniques before the segmentation and classification of images. These preprocessing steps fine-tune the spatial and spectral feature of the data for future processing of the image. The residual characteristics of the image can be retained by calculating local mean and variance matching (LMVM) image fusion technique to retain the spectral information. The proposed study leverages the use of Just Noticeable Difference (JND) model to detect and map the edges of particular point of interest (POI) with reference to the luminance, contrast and structure of the remotely sensed image