Change detection has been defined as a process of ��identifying differences in the state of an object or phenomenon by Erlotinib mechanism of action observing it at different times�� [1]. Various methods have been employed using remotely sensed data for land cover change detection for many decades in urban environments but [1-3]. Those methods may be broadly classified into two categories: pre-classification change detection and post-classification comparison [1, 4].A variety of change detection techniques has been developed for pre-classification change detection, or simultaneous analysis of multitemporal data [1, 3, 4], including image differencing [5], image regression [5], image ratioing [6], vegetation index Inhibitors,Modulators,Libraries differencing [7], principal components analysis [8], change vector analysis [9-10], artificial neural networks [11], and classification tree [12] to name just a few.

These techniques generally Inhibitors,Modulators,Libraries generate ��change�� vs. ��no-change�� maps, but do not specify the type of change [1-2].Post-classification comparison methods detect land cover change by comparing independently produced classifications Inhibitors,Modulators,Libraries of images from different dates [1, 4]. Although the post-classification comparison method requires the classifications of images acquired from different Inhibitors,Modulators,Libraries times, it can not only locate the changes, but also provide ��from-to�� Inhibitors,Modulators,Libraries change information [13-15].

In addition, post-classification comparison minimizes the problems caused Inhibitors,Modulators,Libraries by variation in sensors and atmospheric conditions, as well as vegetation phenology between different dates, since data from different dates are separately Inhibitors,Modulators,Libraries classified [1, 4] and hence reflectance data from those two dates need not be adjusted for direct comparability.

Pixel-based post-classification Dacomitinib comparison has been Inhibitors,Modulators,Libraries widely used for land cover/land use change detection. In particular, this method has been successfully applied for change detection using land cover maps obtained Cilengitide from remotely sensed imagery with coarse or medium spatial resolution [e.g. 14-16]. As the urban environment is extremely complex and heterogeneous, and features are often smaller than the size of a medium-resolution pixel (e.g., buildings and side walks), there is an increasing interest in urban land cover mapping and change detection using high-spatial resolution multispectral imagery from satellite and digital aerial sensors (e.

g., QuickBird from DigitalGlobe, Inc., IKONOS from GeoEys, Inc.

, Emerge from Emerge, Inc.). However, relatively few studies have tested how a pixel-based post-classification selleck Bosutinib comparison approach performs when using very high-spatial resolution imagery.Meanwhile, object-based selleck Temsirolimus image analysis is quickly gaining acceptance among remote sensors and has demonstrated great potential for classification and change detection of high-spatial resolution multispetral imagery in heterogeneous urban environments [e.g. 17-19].