Information Extraction of High-Resolution Remotely Sensed Image based on Multiresolution Segmentation

The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of remotely sensed image based on this principle. The target image was divided into regions based on objectoriented multiresolution segmentation and edge-detection. Further, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information) were extracted by the spectral and geometrical features. The results indicates that edge-detection make a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% through confusion matrix.

[1]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[2]  Zhengjun Liu,et al.  Building extraction from high resolution satellite imagery based on multi-scale image segmentation and model matching , 2008, 2008 International Workshop on Earth Observation and Remote Sensing Applications.

[3]  Marina Mueller,et al.  Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery , 2004, Pattern Recognit..

[4]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[5]  G. Buyuksalih,et al.  OBJECT-ORIENTED IMAGE ANALYSIS AND SEMANTIC NETWORK FOR EXTRACTING THE ROADS AND BUILDINGS FROM IKONOS PAN-SHARPENED IMAGES , 2004 .

[6]  D. Stow,et al.  Object‐based classification of residential land use within Accra, Ghana based on QuickBird satellite data , 2007, International journal of remote sensing.

[7]  John R. Jensen,et al.  Object‐based change detection using correlation image analysis and image segmentation , 2008 .

[8]  Gong Jianya.  Building Boundary Extraction Using Very High Resolution Images and LiDAR , 2008 .

[9]  Gong Peng,et al.  Interpretation Theory and Application Method Development for Information Extraction from High Resolution Remotely Sensed Data , 2006 .

[10]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[11]  M. Herold,et al.  Object-oriented mapping and analysis of urban land use / cover using IKONOS data , 2002 .

[12]  武彦 福島.  持続可能性(Sustainability)の要件 , 2006 .

[13]  M. Baatz,et al.  Object-oriented and Multi-scale Image Analysis in Semantic Networks Introduction: the Necessity for Integration of Remote Sensing and Gis , 2022 .

[14]  Peter Hofmann,et al.  Detecting informal settlements from IKONOS image data using methods of object oriented image analysis—An example from Cape Town (South Africa) , 2001 .

[15]  G. Willhauck,et al.  Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. , 2000 .