In this study, a survey of land cover mapping and their classification techniques is done. Land cover mapping plays a very important role in making land policy, land management and land analysis. In this survey different approaches are studied that were applied for land cover mapping such as an Artificial Neural Network (ANNs), Fuzzy Logic, Supervised, Unsupervised and Maximum Likelihood. The o...

Land cover characterization at a regional scale using spaceborne multi-angle remote sensing data is in its infancy. A data mining technique was employed to evaluate the degree to which the accuracy of land cover classification can be increased using multi-spectral, multi-temporal and multi-angle remote sensing data. The study area is around the Jornada Rangeland in New Mexico, USA with shrublan...

In this paper, the author evaluates the results of applying two evolving classification techniques, decision trees (DT) and artificial neural networks’ back-propagation (ANN-BP), to obtain land cover and land use classes from Quickbird XS data combined with spatial metrics, in a selected urban zone in Bogota, Colombia. In order to improve the quality of the land cover classification, additional...

This paper describes the fusion of information extracted from multispectral digital aerial images for land use classification. The proposed approach integrates spectral classification techniques and spatial information. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and NIR channels and form the basis for information ext...

In this study, maximum likelihood supervised classification and post-classification change detection techniques were applied to Landsat images acquired in 1987 and 2001, respectively, to map land cover changes in the Northwestern coast of Egypt. A supervised classification was carried out on the six reflective bands for the two images individually with the aid of ground truth data. Ground truth...

Land cover and vegetation classification systems are generally designed for ecological or land use applications that are independent of remote sensing considerations. As a result, the classes of interest are often poorly separable in the feature space provided by remotely sensed data. In many cases, ancillary data sources can provide useful information to help distinguish between inseparable cl...

The objective of this research is to evaluate multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using a novel classification scheme. Six-date RADARSAT-2 Polarimetric SAR data in both ascending and descending orbits were acquired during June to September 2008 in the rural-urban fringe of the Greater Toronto Area. The major land-cover types are builtup areas, roa...