Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules
نویسندگان
چکیده
a Department of Geography, University of Alcalá, Alcalá de Henares, 28801 Madrid, Spain b Institute of Economics and Geography, Spanish National Research Council (CSIC), Albasanz 26-28 28037 Madrid, Spain c Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, 250-N, The Barn, One Shields Avenue, Davis, CA 95616-8617, USA d Centre for Environmental Systems Research, School of Environment and Life Sciences, University of Salford, Manchester M5 4WT, UK
منابع مشابه
Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping
This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is t...
متن کاملUrban Vegetation Recognition Based on the Decision Level Fusion of Hyperspectral and Lidar Data
Introduction: Information about vegetation cover and their health has always been interesting to ecologists due to its importance in terms of habitat, energy production and other important characteristics of plants on the earth planet. Nowadays, developments in remote sensing technologies caused more remotely sensed data accessible to researchers. The combination of these data improves the obje...
متن کاملA decision fusion method based on multiple support vector machine system for fusion of hyperspectral and LIDAR data
Fusion of remote sensing data from multiple sensors has been remarkably increased for classification. This is because, additional sources may provide more information, and fusion of different information can produce a better understanding of the observed site. In the field of data fusion, fusion of light detection and ranging (LIDAR) and optical remote sensing data for land cover classification...
متن کاملSupport Vector Machines: Optimization and Validation for Land Cover Mapping Using Aerial Images and Lidar Data
This work investigates the optimization and validation of Support Vector Machines (SVMs) for land cover classification from multispectral aerial imagery and lidar data. For the optimization step, a new method based on a curve fitting technique was applied to minimize the grid search for the Gaussian Radius Basis Function (RBF) parameters. The validation step was based on two experiments. In the...
متن کاملInvestigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification
The abilities of multispectral LiDAR (MSL) as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL include distance information and the intensities of four wavelengths at 556, 670, 700, and 780 nm...
متن کامل