An automated technique to determine spatio-temporal changes in satellite island images with vectorization and spatial queries

For spatio-temporal and topologic analyses, vectorial information (carrying coordinate values defined as point sets) gives better information than its raster (grid of pixels) counterpart. The study presented in this paper is based on (1) recognition and extraction of an island object in a set of digital images captured by LandSat-7 satellite and (2) modelling it as a polygon (vectorial) and making it easy to process and easy to understand for computers and information science applications. Polygon representations of island images then can be stored and manipulated through object-relational spatial databases. Spatial databases have built-in functions and services for spatial objects defined with geometry types such as points, lines, and polygons. By this way we will be utilizing the rapidly changing and developing object-relational database communities’ studies and discoveries in spatio-temporal and topological analysis for the investigation of digital satellite images. This approach also enables service qualities as well as a better performance. The efficiency and feasibility of the proposed system will be examined by various scenarios such as earthquake, erosion and accretion. Scenarios are based on measuring the effects of the natural phenomena on a selected island on satellite images.

[1]  Shijie Cai,et al.  An Object-Oriented Progressive-Simplification-Based Vectorization System for Engineering Drawings: Model, Algorithm, and Performance , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Robert J. Schalkoff,et al.  Digital Image Processing and Computer Vision , 1989 .

[3]  Michihiko Minoh,et al.  Efficient diagram understanding with characteristic pattern detection , 1985, Comput. Vis. Graph. Image Process..

[4]  Ahmet Sayar,et al.  Vectorization and Spatial Query Architecture on Island Satellite Images , 2012 .

[5]  Dov Dori,et al.  Sparse Pixel Vectorization: An Algorithm and Its Performance Evaluation , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Zhang Li-hua.  Straight Line Extraction via Multi-scale Hough Transform Based on Pre-storage Weight Matrix , 2008 .

[7]  Liu Xu-min,et al.  Research on Vectorization of Weather Radar Image , 2011 .

[8]  David B. L. Bong,et al.  Automatic Road Network Recognition and Extraction for Urban Planning , 2009 .

[9]  Dong Du,et al.  Weld Slim Line Defects Extraction Based on Adaptive Local Threshold and Modified Hough Transform , 2009, 2009 2nd International Congress on Image and Signal Processing.

[10]  Chunsun Zhang,et al.  Updating of cartographic road databases by image analysis , 2001 .

[11]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[12]  Ashok Samal,et al.  An Automatic Bridge Detection Technique for Multispectral Images , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Md. Fayzur Rahman,et al.  Automatic Road Extractions from High Resolution Satellite Imagery Using Road Intersection Model in Urban Areas , 2011 .

[14]  Erick López-Ornelas.  High Resolution Images: Segmenting, Extracting Information and GIS Integration , 2009 .

[15]  Ji Ke-Feng,et al.  Road extraction from high-resolution SAR imagery using Hough transform , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[16]  Kavita Tewari,et al.  River extraction from satellite image , 2011 .

[17]  Ashok Samal,et al.  A Statistical Approach for Automatic Detection of Ocean Disturbance Features From SAR Images , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Dov Dori,et al.  From Raster to Vectors: Extracting Visual Information from Line Drawings , 1999, Pattern Analysis & Applications.

[19]  Jose L. Navalón,et al.  A thinning algorithm based on contours , 1987 .

[20]  Juan B. Mena.  Automatic vectorization of segmented road networks by geometrical and topological analysis of high resolution binary images , 2006, Knowl. Based Syst..

[21]  Zhanfeng Shen,et al.  Extraction of bridges over water from IKONOS panchromatic data , 2007 .

[22]  A. Vavilin,et al.  Contour-based algorithm for vectorization of satellite images , 2010, International Forum on Strategic Technology 2010.

[23]  Qiaoping Zhang,et al.  A wavelet approach to road extraction from high spatial resolution remotely-sensed imagery , 2004 .

[24]  M. Haseyama,et al.  An Algorithm for Extracting Lines and Circles with High-Speed and Accuracy , 2003 .

[25]  H. Liu,et al.  Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods , 2004 .

[26]  Ashok Samal,et al.  Semi-Automated Road Detection From High Resolution Satellite Images by Directional Morphological Enhancement and Segmentation Techniques , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Yvonne Koch.  Algorithms For Image Processing And Computer Vision , 2016 .

[28]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Ray A. Jarvis,et al.  On the Identification of the Convex Hull of a Finite Set of Points in the Plane , 1973, Inf. Process. Lett..

[30]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.