scholarly journals Remote Sensing Image Classification of Geoeye-1 High-Resolution Satellite

2014 ◽  
Vol XL-4 ◽  
pp. 325-328
Author(s):  
B. Yang ◽  
X. Yu
Keyword(s):  
High Resolution ◽  
Bayesian Networks ◽  
Satellite Images ◽  
Texture Classification ◽  
Complex Model ◽  
Data Mining Technique ◽  
Probabilistic Networks ◽  
High Resolution Satellite Images ◽  
High Level

Networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Bayesian Networks Augmented Naive Bayes (BAN) to texture classification of High-resolution satellite images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. In the experiment, we choose GeoEye-1 satellite images. Experimental results demonstrate BAN outperform than NBC in the overall classification accuracy. Although it is time consuming, it will be an attractive and effective method in the future.

scholarly journals GRANULOMETRIC MAPS FROM HIGH RESOLUTION SATELLITE IMAGES

2011 ◽  
Vol 21 (1) ◽  
pp. 19 ◽  
Author(s):  
Catherine Mering ◽  
Franck Chopin
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Arid Regions ◽  
Satellite Images ◽  
Distribution Analysis ◽  
Granulometric Analysis ◽  
High Resolution Satellite Images ◽  
Thematic Mapping ◽  
Urban Settlements

A new method of land cover mapping from satellite images using granulometric analysis is presented here. Discontinuous landscapes such as steppian bushes of semi arid regions and recently growing urban settlements are especially concerned by this study. Spatial organisations of the land cover are quantified by means of the size distribution analysis of the land cover units extracted from high resolution remotely sensed images. A granulometric map is built by automatic classification of every pixel of the image according to the granulometric density inside a sliding neighbourhood. Granulometric mapping brings some advantages over traditional thematic mapping by remote sensing by focusing on fine spatial events and small changes in one peculiar category of the landscape.

scholarly journals AUTOMATIC CENTERLINE EXTRACTION OF COVERD ROADS BY SURROUNDING OBJECTS FROM HIGH RESOLUTION SATELLITE IMAGES

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 111-116 ◽  
Author(s):  
H. Kamangir ◽  
M. Momeni ◽  
M. Satari
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Ground Truth ◽  
Road Extraction ◽  
Line Extraction ◽  
Road Map ◽  
High Resolution Satellite Images ◽  
Three Stages ◽  
Classification Of Images

This paper presents an automatic method to extract road centerline networks from high and very high resolution satellite images. The present paper addresses the automated extraction roads covered with multiple natural and artificial objects such as trees, vehicles and either shadows of buildings or trees. In order to have a precise road extraction, this method implements three stages including: classification of images based on maximum likelihood algorithm to categorize images into interested classes, modification process on classified images by connected component and morphological operators to extract pixels of desired objects by removing undesirable pixels of each class, and finally line extraction based on RANSAC algorithm. In order to evaluate performance of the proposed method, the generated results are compared with ground truth road map as a reference. The evaluation performance of the proposed method using representative test images show completeness values ranging between 77% and 93%.

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