ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 165-172, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-7/165/2016/
doi:10.5194/isprs-annals-III-7-165-2016
 
07 Jun 2016
MAPPING OF HIGH VALUE CROPS THROUGH AN OBJECT-BASED SVM MODEL USING LIDAR DATA AND ORTHOPHOTO IN AGUSAN DEL NORTE PHILIPPINES
Rudolph Joshua Candare, Michelle Japitana, James Earl Cubillas, and Cherry Bryan Ramirez Phil-LiDAR2 Project, College of Engineering and Information Technology, Caraga State University, Ampayon, Butuan City, Philippines
Keywords: OBIA, LiDAR ,Orthophoto, Support Vector Machine, eCognition Abstract. This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.
Conference paper (PDF, 5872 KB)


Citation: Candare, R. J., Japitana, M., Cubillas, J. E., and Ramirez, C. B.: MAPPING OF HIGH VALUE CROPS THROUGH AN OBJECT-BASED SVM MODEL USING LIDAR DATA AND ORTHOPHOTO IN AGUSAN DEL NORTE PHILIPPINES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 165-172, doi:10.5194/isprs-annals-III-7-165-2016, 2016.

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