Per-pixel Classification of High Spatial Resolution Satellite Imagery for Urban Land-cover Mapping

2008 ◽  
Vol 74 (4) ◽  
pp. 463-471 ◽  
Author(s):  
David Barry Hester ◽  
Halil I. Cakir ◽  
Stacy A.C. Nelson ◽  
Siamak Khorram
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Pixel Classification ◽  
Urban Land Cover

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 OBJECT BASED IMAGE ANALYSIS COMBINING HIGH SPATIAL RESOLUTION IMAGERY AND LASER POINT CLOUDS FOR URBAN LAND COVER

2016 ◽  
Vol XLI-B3 ◽  
pp. 733-739 ◽  
Author(s):  
Xiaoliang Zou ◽  
Guihua Zhao ◽  
Jonathan Li ◽  
Yuanxi Yang ◽  
Yong Fang
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Point Clouds ◽  
Urban Land ◽  
Airborne Lidar ◽  
Image Objects ◽  
Urban Land Cover ◽  
High Resolution Imagery ◽  
High Spatial Resolution Imagery

With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.

scholarly journals OBJECT BASED IMAGE ANALYSIS COMBINING HIGH SPATIAL RESOLUTION IMAGERY AND LASER POINT CLOUDS FOR URBAN LAND COVER

2016 ◽  
Vol XLI-B3 ◽  
pp. 733-739
Author(s):  
Xiaoliang Zou ◽  
Guihua Zhao ◽  
Jonathan Li ◽  
Yuanxi Yang ◽  
Yong Fang
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Point Clouds ◽  
Urban Land ◽  
Airborne Lidar ◽  
Image Objects ◽  
Urban Land Cover ◽  
High Resolution Imagery ◽  
High Spatial Resolution Imagery

With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.

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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