Change Detection from Landsat TM Images Using Change Vector Analysis

Xiao Lu Song; Bo Cheng; Teng Fei Long

doi:10.4028/www.scientific.net/AMR.268-270.590

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 268-270Change Detection from Landsat TM Images Using...

Change Detection from Landsat TM Images Using Change Vector Analysis

Abstract:

Change detection is a process of extracting, analyzing, and defining change information from remote sensing imageries. At present, remote sensing change detection methods are mainly classified into two categories, one based on the spectral characteristics of the type of approach, and the other is spectral change vector analysis. In this paper, a simplified threshold variable step-size search which can be used on determining changes in the threshold vector is adopted, as well as the supervised classification technique in the direction cosine space with the type of focal point in the initial assay vector remote sensing images. Results are discussed in the last part of this paper, which show that CVA can extract change information effectively in our study area of Wuhan city.

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Periodical:

Advanced Materials Research (Volumes 268-270)

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590-594

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.268-270.590

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Online since:

July 2011

Authors:

Xiao Lu Song, Bo Cheng, Teng Fei Long

Keywords:

Change Detection, Change Vector Analysis, Direction Cosine Vector Space, Threshold Search

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References

[1] Wafa Nori, Hussein M. Sulieman, Irmgard Niemeyer, Detection of Land Cover Changes in EL Rawashda Forest, Sudan: A Systematic Comparision, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 1, pp. I88-I91, 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009- Proceedings.

DOI: 10.1109/igarss.2009.5416935

Google Scholar

[2] ZHANG Zhen-long, ZENG Zhi-yuan, LI Shuo, HU Zi-fu, A Summary of Change Detection Methods of Remote Sensing Image, Remote Sensing Information, vol. 5, pp.64-66, (2005).

Google Scholar

[3] W. A. Malila, Change vector analysis: An approach for detecting forest changes with Landsat, in Proc. 6th Annu. Symp. Machine Processing of Remotely Sensed Data, pp.326-335, (1980).

Google Scholar

[4] Jensen J R. Introductory Digital Image Processing, a Remote Sensing Perspective. Prentice Hall, Upper Saddle River, New Jersey, 2nd ED, pp.197-279. (1996).

Google Scholar

[5] R. B. Lorena, J. R. Santos, Y. E. Shimabukuro, I. F. Brown, and H. J. H. Kux, A change vector analysis technique to monitor land use/land cover in sw Brazilian amazon: Acre state, in PECORA 15-Integrating Remote Sensing at the Global, Regional and Local Scale, Denver, CO, USA, 2002, pp.8-15.

DOI: 10.1080/07038992.1998.10874699

Google Scholar

[6] CHEN Jin, HE Chun yang, SHI Pei jun, CHEN Yun hao, Ma Nan, Land Use/Cover Change Detection with Change Vector Analysis (CVA): Change Magnitude Threshold Determination, Journal of Remote Sensing, vol. 4, pp.259-266, May (2001).

DOI: 10.14358/pers.69.4.369

Google Scholar

[7] CHEN Jin, HE Chun-yang, ZHUO Li, Land Use/Cover Change Detection with Change Vector Analysis (CVA): Change Type Determining, Journal of Remote Sensing, vol. 5, pp.346-352, May (2001).

Google Scholar

[8] E.F. Lambin and A.H. Strahler, Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change processes using high temporalresolution satellite data, Remote Sensing of Environment, vol. 48, pp: 231–244, (1994).

DOI: 10.1016/0034-4257(94)90144-9

Google Scholar