An Object-Based Classification Approach in Mapping Tree Mortality Using High Spatial Resolution Imagery-Reference-Cited by-同舟云学术

An Object-Based Classification Approach in Mapping Tree Mortality Using High Spatial Resolution Imagery

Published:2007-03 Issue:1 Volume:44 Page:24-47
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Guo Qinghua¹,Kelly Maggi²,Gong Peng²,Liu Desheng³

Affiliation:

1. a University of California, Merced, California

2. b University of California, Berkeley, California

3. c The Ohio State University, Columbus, Ohio

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.2747/1548-1603.44.1.24

Reference58 articles.

1. Arbia, G., Benedetti, R., and G. Espa, 1999, "Contextual Classification in Image Analysis: An Assessment of Accuracy of ICM," Computational Statistics & Data Analysis 30:443-455.

2. Asner, G. P., Keller, M., Rodrigo Pereira, J., Zweede, J. C., and J. N. M. Silva, 2004, "Canopy Damage and Recovery after Selective Logging in Amazonia: Field and Satellite," Ecological Applications 14:S280-S298.

3. Baatz, M. and A. Schape, 2000, "Multiresolution Segmentation: An Optimization Approach for High Quality Multi-scale Image Segmentation," Angewandte Geographische Informationsverarbeitung 12:12-23.

4. Baraldi, A. and F. Parmiggiani, 1996, "Single Linkage Region Growing Algorithms Based on the Vector Degree of Match," IEEE Transactions on Geoscience and Remote Sensing 34:137-148.

5. Bardossy, A. and L. Samaniego, 2002, "Fuzzy Rule-Based Classification of Remotely Sensed Imagery," IEEE Transactions on Geoscience and Remote Sensing 40:362-374.

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