High Resolution Satellite Images Classification

In order to verify the validity of object-oriented Classification for high resolution satellite imagery, a scene of 2.4-meter multispectral image of Quickbird is .
Abstract. In this thesis the Support Vector Machine (SVM) is applied on classification of high resolution satellite images. Several different measures for .

Traditional classification algorithms are not suitable for feature extraction on high resolution satellite images, given the heterogeneity of the pixels of this type of .
The aim of this study is to explore the viability of applying high resolution satellite imagery for inland limnetic wetlands cover classification using object-oriented .
In this study, we have looked into the problem of vehicle detection in high-resolution satellite images. Based on the input from the local road authorities, we have .
The commonly used automatic methods of satellite image classification, based on. . devised mainly with the classification of high resolution satellite images, as.
ECHO spectral-spatial image classification algorithm and the Maximum Likelihood technique. The. ing low-resolution satellite data with high-resolution.
Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical .
ABSTRACT: Recent access to Very High Spatial Resolution (VHSR) Satellite Images allows vegetation moni- toring at metric and sub-metric scale, with .

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