Remote sensing of tea plantations using an SVM classifier and pattern-based accuracy assessment technique-Reference-Cited by-同舟云学术

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Remote sensing of tea plantations using an SVM classifier and pattern-based accuracy assessment technique

Published:2013-10-07 Issue:23 Volume:34 Page:8549-8565
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Dihkan Mustafa¹,Guneroglu Nilgun²,Karsli Fevzi¹,Guneroglu Abdulaziz³

Affiliation:

1. Department of Geomatics, Faculty of Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey

2. Department of Landscape Architecture, Faculty of Forestry, Karadeniz Technical University, 61080 Trabzon, Turkey

3. Department of Ecology, Faculty of Marine Science, Karadeniz Technical University, 61530 Trabzon, Turkey

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2013.845317

Reference67 articles.

1. Automatic Mapping of Linear Woody Vegetation Features in Agricultural Landscapes Using Very High Resolution Imagery

2. Automatic Detection and Segmentation of Orchards Using Very High Resolution Imagery

3. Texture classification using wavelet transform

4. Introduction Neural networks in remote sensing

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