Sensors 2008, 8(7), 4308-4317; doi:10.3390/s8074308
Article

A Fixed-Threshold Approach to Generate High-Resolution Vegetation Maps for IKONOS Imagery

1 Department of Electrical Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335, Taiwan, R.O.C. 2 Department of Information Communications, Kainan University, Taoyuan 338, Taiwan, R.O.C.
* Author to whom correspondence should be addressed.
Received: 18 May 2008; in revised form: 16 July 2008 / Accepted: 18 July 2008 / Published: 25 July 2008
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Abstract: Vegetation distribution maps from remote sensors play an important role in urban planning, environmental protecting and related policy making. The normalized difference vegetation index (NDVI) is the most popular approach to generate vegetation maps for remote sensing imagery. However, NDVI is usually used to generate lower resolution vegetation maps, and particularly the threshold needs to be chosen manually for extracting required vegetation information. To tackle this threshold selection problem for IKONOS imagery, a fixed-threshold approach is developed in this work, which integrates with an extended Tasseled Cap transformation and a designed image fusion method to generate high-resolution (1-meter) vegetation maps. Our experimental results are promising and show it can generate more accurate and useful vegetation maps for IKONOS imagery.
Keywords: IKONOS; NDVI; Tasseled Cap transformation; image fusion.

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MDPI and ACS Style

Cheng, W.-C.; Chang, J.-C.; Chang, C.-P.; Su, Y.; Tu, T.-M. A Fixed-Threshold Approach to Generate High-Resolution Vegetation Maps for IKONOS Imagery. Sensors 2008, 8, 4308-4317.

AMA Style

Cheng W-C, Chang J-C, Chang C-P, Su Y, Tu T-M. A Fixed-Threshold Approach to Generate High-Resolution Vegetation Maps for IKONOS Imagery. Sensors. 2008; 8(7):4308-4317.

Chicago/Turabian Style

Cheng, Wen-Chun; Chang, Jyh-Chian; Chang, Chien-Ping; Su, Yu; Tu, Te-Ming. 2008. "A Fixed-Threshold Approach to Generate High-Resolution Vegetation Maps for IKONOS Imagery." Sensors 8, no. 7: 4308-4317.

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