Mining patterns of change in remote sensing image databases

Remote sensing image databases are the fastest growing archives of spatial information. However, we still have a limited capacity for extracting information from large remote sensing image databases. There are currently very few techniques for image data mining and information extraction in large image data sets, and thus we are failing to exploit our large remote sensing data archives. This paper proposes a methodology to provide guidance for mining remote sensing image databases. The basic idea is to use domain concepts to build generic description of patterns in remote sensing images, and then use structural approaches to identify such patterns in images. We illustrate our proposal with a case study for detecting land use patterns in Amazonia from INPE's remote sensing image database.

[1]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Guaraci J. Erthal,et al.  Satellite Imagery Segmentation: a region growing approach , 1996 .

[3]  Gilberto Câmara,et al.  Spring: integrating remote sensing and gis by object-oriented data modelling , 1996, Comput. Graph..

[4]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[5]  M. Neubert,et al.  A COMPARISON OF SEGMENTATION PROGRAMS FOR HIGH RESOLUTION REMOTE SENSING DATA , 2004 .

[6]  Selim Aksoy,et al.  Interactive training of advanced classifiers for mining remote sensing image archives , 2004, KDD.

[7]  E. Lambin,et al.  Dynamics of Land-Use and Land-Cover Change in Tropical Regions , 2003 .

[8]  Steven W. Zucker,et al.  Region growing: Childhood and adolescence* , 1976 .

[9]  Yixin Chen,et al.  Image Categorization by Learning and Reasoning with Regions , 2004, J. Mach. Learn. Res..

[10]  Yixin Chen,et al.  CLUE: cluster-based retrieval of images by unsupervised learning , 2005, IEEE Transactions on Image Processing.

[11]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Otthein Herzog,et al.  Content-based Image Retrieval by Ontology-based Object Recognition , 2004 .

[13]  Marco Pastori,et al.  Information Mining in Remote Sensing Image Archives — Part A : System Concepts , 2003 .

[14]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[15]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[16]  Marco Pastori,et al.  Information mining in remote sensing image archives: system concepts , 2003, IEEE Trans. Geosci. Remote. Sens..

[17]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[18]  D. S. Alves,et al.  Land use intensification and abandonment in Rondônia, Brazilian Amazônia , 2003 .

[19]  M. Turner,et al.  LANDSCAPE ECOLOGY : The Effect of Pattern on Process 1 , 2002 .

[20]  A. Pekkarinen,et al.  A method for the segmentation of very high spatial resolution images of forested landscapes , 2002 .

[21]  Latifur Khan,et al.  Object Boundary Detection For Ontology-Based Image Classification , 2002, MDM/KDD.

[22]  Y. Shimabukuro.  Using shade fraction image segmentation to evaluate deforestation in Landsat Thematic Mapper images , 1998 .

[23]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[24]  Frederico T. Fonseca,et al.  What's in an Image? , 2001, COSIT.

[25]  Mihai Datcu,et al.  Interactive learning and probabilistic retrieval in remote sensing image archives , 2000, IEEE Trans. Geosci. Remote. Sens..

[26]  Rahul Ramachandran,et al.  ADaM: a data mining toolkit for scientists and engineers , 2005, Comput. Geosci..

[27]  Frederico T. Fonseca,et al.  Using Ontologies for Integrated Geographic Information Systems , 2002, Trans. GIS.

[28]  K. McGarigal,et al.  FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. , 1995 .