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Utilization of Pisar L-2 Data for Land Cover Classification in Forest Area Using Pixel-based and Object-based Methods : Volume Xl-7/W3, Issue 1 (29/04/2015)

By Trisakti, B.

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Book Id: WPLBN0004015966
Format Type: PDF Article :
File Size: Pages 5
Reproduction Date: 2015

Title: Utilization of Pisar L-2 Data for Land Cover Classification in Forest Area Using Pixel-based and Object-based Methods : Volume Xl-7/W3, Issue 1 (29/04/2015)  
Author: Trisakti, B.
Volume: Vol. XL-7/W3, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus Publications
Historic
Publication Date:
2015
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Kustiyo,, Trisakti, B., Noviar, H., & Sutanto, A. (2015). Utilization of Pisar L-2 Data for Land Cover Classification in Forest Area Using Pixel-based and Object-based Methods : Volume Xl-7/W3, Issue 1 (29/04/2015). Retrieved from http://www.ebooklibrary.org/


Description
Description: Indonesian National Institute of Aeronautics and Space (LAPAN), Jakarta, Indonesia. Polarimetric and Interferometric Airborne SAR in L-band 2 (PiSAR-L2) program is an experimental program of PALSAR-2 sensor in ALOS-2 satellite. Japan Aerospace Exploration Agency (JAXA) and Indonesian National Institute of Aeronautics and Space (LAPAN) have a research collaboration to explore the utilization of PiSAR-L2 data for forestry, agriculture, and disaster applications in Indonesia. The research explored the utilization of PiSAR-L2 data for land cover classification in forest area using the pixel-based and object-based methods. The PiSAR-L2 data in the 2.1 level with full polarization bands were selected over part of forest area in Riau Province. Field data collected by JAXA team was used for both training samples and verification data. Preprocessing data was carried out by backscatter (Sigma naught) conversion and Lee filtering. Beside full polarization images (HH, HV, VV), texture imagess (HH deviation, HV deviation, and VV deviation) were also added as the input bands for the classification processes. These processes were conducted for 2.5 meter and 10 meter spatial resolution data applying two methods of the maximum likelihood classifier for pixel-based classification and the support vector machine classifier for the object-based classification. Moreover, the average overall accuracy was calculated for each classification result. The results show that the use of texture images could improve the accuracy of land cover classification, particularly to differentiate between forest and acacia plantation. The pixelbased method showed a more detail information of the objects, but has “salt and pepper”. In the other hand, the object-based method showed a good accuracy and clearer border line among objects, but has often some misinterpretations in object identification.

Summary
Utilization of Pisar L-2 Data for Land Cover Classification in Forest Area Using Pixel-Based and Object-Based Methods

 

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