Object-oriented classification of land use/cover using digital aerial orthophotography

In automatic/semiautomatic mapping of land use/cover using very high resolution remote-sensing imagery, the major challenge is that a single class of land use contains ground targets with varied spectral values, textures, geometries and spatial features. Here we present an object-oriented strategy for automatic/semiautomatic classifications of land use/cover using very high resolution remote-sensing data. The strategy consists of character detecting, object positioning and coarse classification, then refining the classification result step by step. The strategy combines the form classification of the objects located on the same level by using spectral values, textures and geometric features with function classification by using spatial logic relationships existing among the objects on the same level or between different levels. Furthermore, it overcomes the problem of transformation from form classification to function classification and unifies land use classification and land cover classification organically. Such an approach not only achieves high classification accuracy, but also avoids the salt-and-pepper effect found in conventional pixel-based procedures. The borderlines of the classification result are clear, the patches are pure, and the classification objects exactly match the ground targets distributed across the study site. A feasible technical strategy for the large-scale application is discussed in this article.

[1]  Guofan Shao,et al.  An explicit index for assessing the accuracy of cover-class areas , 2003 .

[2]  Manoj K. Arora,et al.  Estimating and accommodating uncertainty through the soft classification of remote sensing data , 2005 .

[3]  Yerach Doytsher,et al.  Geographic Information System Data for Supporting Feature Extraction from High-Resolution Aerial and Satellite Images , 2003 .

[4]  Xie Yuan-dan,et al.  Survey on Image Segmentation , 2002 .

[5]  Manfred Ehlers,et al.  Automated analysis of ultra high resolution remote sensing data for biotope type mapping: new possibilities and challenges , 2003 .

[6]  Chad Hendrix,et al.  A Comparison of Urban Mapping Methods Using High-Resolution Digital Imagery , 2003 .

[7]  F. Hájek OBJECT-ORIENTED CLASSIFICATION OF REMOTE SENSING DATA FOR THE IDENTIFICATION OF TREE SPECIES COMPOSITION , 2005 .

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

[9]  K. Johnsson Segment-based land-use classification from SPOT satellite data , 1994 .

[10]  S. Franklin,et al.  OBJECT-BASED ANALYSIS OF IKONOS-2 IMAGERY FOR EXTRACTION OF FOREST INVENTORY PARAMETERS , 2006 .

[11]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[12]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[13]  D. P. Paine,et al.  Aerial Photography and Image Interpretation for Resource Management , 1981 .

[14]  T. Bauer,et al.  Per-parcel land use classification in urban areas applying a rule-based technique , 2001 .

[15]  Paul Aplin,et al.  Sub-pixel land cover mapping for per-field classification , 2001 .

[16]  G. Schreier,et al.  OSCAR-object oriented segmentation and classification of advanced radar allow automated information extraction , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[17]  Alexis J. Comber,et al.  Application of knowledge for automated land cover change monitoring , 2004 .

[18]  King-Sun Fu,et al.  A survey on image segmentation , 1981, Pattern Recognit..

[19]  Peng Gong,et al.  A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data , 1992 .

[20]  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 .

[21]  Arthur P Cracknell,et al.  The IPCC Fourth Assessment Report and the fiftieth anniversary of Sputnik. , 2007, Environmental science and pollution research international.

[22]  C. Kontoes,et al.  The potential of kernel classification techniques for land use mapping in urban areas using 5m-spatial resolution IRS-1C imagery , 2000 .

[23]  Jinmu Choi,et al.  A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images , 2004 .

[24]  Martin Herold,et al.  Spectral resolution requirements for mapping urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..

[25]  Darrel L. Williams,et al.  The effects of spatial resolution on the classification of Thematic Mapper data , 1985 .

[26]  R. Platt,et al.  An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification , 2008 .

[27]  A. Rango,et al.  Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico , 2004 .

[28]  Nikhil R. Pal,et al.  Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Curtis E. Woodcock,et al.  Nested-hierarchical scene models and image segmentation , 1992 .

[30]  G. J. Hay,et al.  A multiscale framework for landscape analysis: Object-specific analysis and upscaling , 2001, Landscape Ecology.

[31]  Mark H. Hansen,et al.  Using a land cover classification based on satellite imagery to improve the precision of forest inventory area estimates , 2002 .

[32]  A. Troy,et al.  An object‐oriented approach for analysing and characterizing urban landscape at the parcel level , 2008 .

[33]  Hugh G. Lewis,et al.  Superresolution mapping using a hopfield neural network with fused images , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Alexandre Carleer,et al.  Assessment of Very High Spatial Resolution Satellite Image Segmentations , 2005 .

[35]  David A. Landgrebe,et al.  Signal Theory Methods in Multispectral Remote Sensing , 2003 .

[36]  Tor-Gunnar Vågen,et al.  Remote sensing of complex land use change trajectories—a case study from the highlands of Madagascar , 2006 .

[37]  G. Shao,et al.  On the accuracy of landscape pattern analysis using remote sensing data , 2008, Landscape Ecology.