Classification of urban land cover based on expert systems, object models and texture

1997 ◽  
Vol 21 (3-4) ◽  
pp. 291-302 ◽  
Author(s):  
Lasse Moller-Jensen
Keyword(s):  
Land Cover ◽  
Expert Systems ◽  
Urban Land ◽  
Urban Land Cover ◽  
Object Models

scholarly journals ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1417-1421
Author(s):  
D. Amarsaikhan
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Landsat 8 ◽  
Data Sets ◽  
Contextual Knowledge ◽  
Rule Based ◽  
Land Cover Types ◽  
Urban Land Cover ◽  
Land Cover Map

Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.

Classification of Polarimetric SAR Data Based on Object-Based Multiple Classifiers for Urban Land-Cover

2016 ◽  
Vol 44 (6) ◽  
pp. 855-863 ◽  
Author(s):  
Masoud Habibi ◽  
Mahmod Reza Sahebi ◽  
Yasser Maghsoudi ◽  
Shaheen Ghayourmanesh
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Polarimetric Sar ◽  
Multiple Classifiers ◽  
Urban Land Cover ◽  
Object Based ◽  
Sar Data

Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery

2011 ◽  
Vol 115 (5) ◽  
pp. 1145-1161 ◽  
Author(s):  
Soe W. Myint ◽  
Patricia Gober ◽  
Anthony Brazel ◽  
Susanne Grossman-Clarke ◽  
Qihao Weng
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Urban Land ◽  
Urban Land Cover ◽  
Object Based ◽  
High Spatial Resolution Imagery

scholarly journals Performance of Combination of Texture and Object Based Techniques in Image Classification for Urban Land Cover

2016 ◽  
Vol 3 (3) ◽  
Author(s):  
J. Jacinth Jennifer
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Satellite Imagery ◽  
Technology Development ◽  
Urban Land ◽  
Urban Land Cover ◽  
High Resolution Imagery ◽  
Object Based ◽  
Texture Parameters

<div><p class="IJARCSAbstract"><em>Satellite imagery paves way to obtain tangible information through remote sensing techniques.  It is necessary to classify the image in order to extract the features.  There exist various classification techniques and algorithms to retrieve various features from imagery.  As the technology development proceeds in a faster track it is necessary to compensate its advancements by developing new techniques for feature retrieval.  As far as high resolution satellite imagery are concerned object based feature retrieval and texture based feature retrieval techniques are gaining its importance.  The texture based feature retrieval has various techniques involved in it, among which Haralick’s texture parameters has much importance.  Thereby object based technique also has its own way of algorithms and processes for feature retrieval.  The eCognition software provides a platform for combining texture and object based technique.  It is well known from various journals that object based technique is best for classifying high resolution imagery.  Thus the image is primarily segmented into objects for classification.  The Haralick’s texture parameters which serve well in classification of urban land cover is chosen by computing statistical analysis.  Finally the chosen texture parameter is adopted in the classification of the objects.  The classified imagery is checked for accuracy and a high accuracy of 94.5% is obtained.</em></p></div>

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