Automatic Feature Extraction Module for Change Detection in Al Ain, UAE: Analysis by Means of Multi-temporal Remote Sensing Data

Abstract

Monitoring new changes in cities adjacent to dynamic sand dunes requires precise classifier technique. Unlike traditional techniques of supervised classification which use training sites, the integration of image transformation tasseled cap and automatic feature extraction module based on spectral signatures has provided to be sensitive and realistic techniques with time and cost effective. The proposed module was applied to Al Ain district, United Arab Emirates (UAE). The module consists of four steps in terms of segmentation, thresholding and clustering and computing attributes. The obtained greenness and classified maps were then enhanced by applying a 3 × 3 Sobel filter. The new changes were detected by combining the multi-temporal greenness and classification maps. Accuracy assessment and quantitative analysis were performed using confusion matrix and ground truthing. The results showed significant increasing in urban and agricultural areas from the year from 1990 to 2000 compared with the period of time from the year 2000 to 2006. The image difference showed that the vegetation and building classes had increased 7.58 and 20.28 km2 respectively. This study showed that image difference and fuzzy logic approach are the most sensitive techniques for detecting new changes in areas adjacent to dynamic sand dunes.