An object-based classification approach for high-spatial resolution Imagery

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 6752, Issue PART 2, 2007, Pages

  Li, Xinliang ^a   Zhao, Shuhe ^a   Rui, Yikang ^a   Tang, Wei ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

Classification; Contextual relation; Image segmentation; Object based feature; QuickBird imagery; The nearest neighbor approach

Indexed keywords

FEATURE EXTRACTION; IMAGE SEGMENTATION; LAND USE; PIXELS; REMOTE SENSING;

CONTEXTUAL RELATION; OBJECT-BASED FEATURE; QUICKBIRD IMAGERY;

IMAGE CLASSIFICATION;

EID: 36248987752     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.761260     Document Type: Conference Paper

References (16)

1. J. H. Zhang, C. Y. He and Y. Z. Pan et al. "The High Spatial Resolution RS Image Classification Based on SVM Method with the Multi-Source Data," Journal of Remote Sensing.2006, 10(1):49-57.
2. Fritz L W, "The Era of Commericial Earth Observation Satellites," PE & RS. 1996, 62 (1): 39-45.
3. P. Gong, X. Li and B. Xu. "Interpretation Theory and Application Method Development for Information Extraction from High Resolution Remotely sensed Data," Journal of Remote Sensing. 2006, 10(1): 1-5.
4. P. Gong, Marceau D and Howarth P J. "A Comparison of Spatial Feature Extraction Algorithms for Land-use Mapping with SPOT HRV Data," Remote Sensing of Environment. 1992, 40:137-151.
5. C. Q. Chengqi and T. Ma. "Automatic Recognition of Landscape Linear Features from High- Resolution Satellite Images," Journal of Remote Sensing. 2003, 7 (1): 26-30.
6. C. Y. He, X, Cao and P. J. Shi et al. "Incorporation of Texture and Structure Information for Urban Building Detection by Using Landsat7 ETM+ Panchromatic Image," Geomatics and Information Science of Wuhan University. 2004, 29 (9):800-804.
7. H. P. Huang, B. F. Wu and M. M. Li et al. "Detecting Urban Vegetation Efficiently with High Resolution Remote Sensing Data," Journal of Remote Sensing. 2004, 8(1): 68-74.
8. Y. Zhang, "Optimisation of Building Detection in Satellite Images by Combining Multispectral Classification and Texture Filtering," PE & RS. 1999, 54: 50-60.
9. A.Bardossy and L.Samaniego, "Fuzzy rule-based classification of remotely sensed imagery.IEEE Trans. Geosci," Remote Sensing, vol. 40, pp. 362-374, Feb. 2002.
10. C. Burnett and Thomas Blaschke, "A multi-scale segmentation/object relationship modeling methodology for landscape analysis," Ecological Modelling. 168 (2003) 233-249.
11. Aaron K. Shackelford and Curt H. Davis, "A Combined Fuzzy Pixel-Based and Object-Based Approach for Classification of High-Resolution Multi-spectral Data Over Urban Areas," IEEE Trans. Geosci. Remote Sensing, vol. 41, no. 10, pp.2354-2363, oct. 2003.
12. B. Cao, Q. M. Qin and H. J. Ma et al, "Application of Object-Oriented Approach to SPOT5 Image Classification: A Case Study in Haidian District, Beijing City," Geography and Geo-information Science.2006, 22(2):46-54.
13. X. Cao and C. Q. Ke. "Classification of High-resolution Remote Sensing Images Using Object-oriented Method," Remote Sensing Information.2006 (5):27-30.
14. Haralick, R., Shanmugan, K. and Dinstein, I., "Textural features for image classification," IEEE Trans. Systems Man Cybernetics. 1973:3 (1), 610-621.
15. Baatz, M. and Schape, "Multiresolution segmentation an optimization approach for high quality multi-scale image segmentation.In: Strobl, J.and Blaschke, T. (eds.): Angewandte Geographic Information sverarbeitung, "Wichmann Verlag.2000:12-23.
16. X. Li, "A New Method to Improve Classification Accuracy with Shape Information," Remote Sensing of Environment China.1995.10(4):279-2.