The aim of this study is to discriminate urban land cover types using the features derived from optical and spaceborne interferometric synthetic aperture radar (InSAR) data sets. For the efficient discrimination of the selected classes, a rule-based algorithm that uses the initial image segmentation procedure based on a Mahalanobis distance rule and the constraints on spectral parameters and sp...

not been submitted before for any degree or examination in any other University. Abstract This study presents experimental investigations on supervised ensemble classification for land cover classification. Despite the arrays of classifiers available in machine learning to create an ensemble, knowing and understanding the correct classifier to use for a particular dataset remains a major challe...

This document details the structural definition, development process, and functional flow of the MODIS Land-Cover Product. The Land Cover and Land-Cover Change Parameters were proposed by the MODIS Land Team, with Team Member Alan Strahler leading the effort. The Land Cover Parameter is a 1-km product provided on a quarterly basis beginning about one year following the acquisition of a global d...

This study aims to investigate the characteristics of LIDAR intensity data for executing land cover classification. The quantitative and qualitative analysis was executed with reflection geometry model and land cover classification data. By these analyses, the LIDAR intensity feasibility for classification was discussed. The survey found that intensity is inversely proportional to angle and dis...

This paper discusses the use of a JERS-1 Synthetic Aperture Radar (SAR) time-series for mapping and monitoring land cover in a test site in the region of Corumbiara, Rondônia State, western Brazilian Amazônia. In order to support JERS-1 data analysis, land cover maps were obtained by digital classification of Landsat TM images acquired from 1993 to 1997 period, following a procedure based on im...

A set of ERS SAR and optical MODIS-images were classified to land cover and tree species classes. Different methods for pixel and decision based data fusion were tested. Classifications of featuresets were carried out using Bayes rule for minimum error. The results were not very successful, the classification accuracies of land cover classes varied from 43% to 75%, depending on the used feature...

Detailed and precise information of land-use and land-cover (LULC) in rural area is essential for land-use planning, environment and energy management. The confusion in mapping residential and industrial areas brings problems in energy management, environmental management and sustainable land use development. However, they remain ambiguous in the former rural LULC mapping, and this insufficient...

Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging) and CASI (Compact Airborne Spectrographic Imager) hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity) and CASI data (48 bands) with 1 m spat...

Global urban expansion has created incentives to convert green spaces to urban/built-up area. Therefore, understanding the distribution and dynamics of the land-cover changes in cities is essential for better understanding of the cities’ fundamental characteristics and processes, and of the impact of changing land-cover on potential carbon storage. We present a grid square approach using multi-...