Segmentation and classification of high resolution imagery for mapping individual species in a closed canopy, deciduous forest

2006 ◽  
Vol 49 (S1) ◽  
pp. 128-139 ◽  
Author(s):  
Timothy A. Warner ◽  
James B. McGraw ◽  
Rick Landenberger
Keyword(s):  
High Resolution ◽  
Deciduous Forest ◽  
Individual Species ◽  
High Resolution Imagery ◽  
Closed Canopy

Rule-based classification of high-resolution imagery over urban areas in New York City

2013 ◽  
Vol 28 (6) ◽  
pp. 527-545 ◽  
Author(s):  
Sunil Bhaskaran ◽  
Eric Nez ◽  
Karolyn Jimenez ◽  
Sanjiv K. Bhatia
Keyword(s):  
New York ◽  
New York City ◽  
High Resolution ◽  
York City ◽  
Urban Areas ◽  
Rule Based ◽  
High Resolution Imagery

scholarly journals AUTOMATIC EXTRACTION OF BUILDING OUTLINE FROM HIGH RESOLUTION AERIAL IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 419-423 ◽  
Author(s):  
Yandong Wang
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Automatic Generation ◽  
Aerial Imagery ◽  
Building Detection ◽  
New Approach ◽  
Building Roof ◽  
High Resolution Imagery ◽  
Point Cloud Classification

In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately. It consists of automatic generation of high quality point cloud from the imagery, building detection from point cloud, classification of building roof and generation of building outline. Point cloud is generated from the imagery automatically using semi-global image matching technology. Buildings are detected from the differential surface generated from the point cloud. Further classification of building roof is performed in order to generate accurate building outline. Finally classified building roof is converted into vector format. Numerous tests have been done on images in different locations and results are presented in the paper.

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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