OPTIMASI PARAMETER DALAM KLASIFIKASI SPASIAL PENUTUP PENGGUNAAN LAHAN MENGGUNAKAN DATA SENTINEL SAR (PARAMETERS OPTIMIZATION IN SPATIAL LAND USE LAND COVER CLASSIFICATION USING SENTINEL SAR DATA)
Abstract
In this study, application of Sentinel-1 Synthetic Aperture Radar (SAR) data for the land use cover classification was investigated. The classification was implemented with supervised Neural Network classifier for Dual polarization (VH and VV) Sentinel-1 data using texture information of gray level co-occurance matrix (GLCM). The purpose of this study was to obtain the optimum parameters in the extraction of texture information of pixel window size, the orientation of neighboring relationships on the texture feature extraction, and the type of texture information feature used for the classification. The classification results showed that in the study area, the best accuracy obtained is 5 × 5 pixel window size, 00 orientation angle, and the use of entropy texture information as classification input. It was also found that more features texture information used as classification input can improve the accuracy, and with careful selection of appropriate texture information as classification input will give the best accuracy.
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Abstrak
Pada penelitian ini dilakukan kajian mengenai klasifikasi penutup penggunaan lahan menggunakan data Sentinel-1 yang merupakan data Synthetic Aperture Radar (SAR). Informasi tekstur digunakan sebagai masukan dalam pembuatan klasifikasi terbimbing Neural Network dengan menggunakan Dual polarization (VH dan VV). Klasifikasi dilakukan menggunakan informasi tekstur menggunakan Gray Level Co-occurance Matrix (GLCM) dari data Sentinel-1. Tujuan penelitian ini adalah mendapatkan parameter optimum dalam ekstraksi informasi, yaitu ukuran jendela pemrosesan, orientasi hubungan ketetanggaan pada ekstraksi fitur tekstur, serta jenis fitur informasi tekstur yang digunakan dalam klasifikasi. Hasil klasifikasi menunjukkan bahwa pada area yang dikaji, akurasi terbaik adalah pada ukuran jendela 5×5 piksel, sudut orientasi hubungan ketetanggaan 0º, serta penggunaan informasi tekstur entropy sebagai masukan dalam klasifikasi. Serta diketahui bahwa semakin banyak fitur informasi tekstur yang digunakan sebagai masukan klasifikasi dapat meningkatkan akurasi dan pemilihan informasi tekstur yang tepat sebagai masukan klasifikasi akan menghasilkan akurasi terbaik.
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