3 research outputs found

    Ekstraksi Jalan Raya Berdasarkan Pendeteksian Zebra Cross Dari Foto Udara Beresolusi Sangat Tinggi Dan Data DSM

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    Terdapat beberapa strategi pada proses ekstraksi jalan seperti segmentasi citra, deteksi garis, evolusi kurva, multi-resolution, kecerdasan buatan, deteksi tepi, roadtracking, operasi morfologi, pengenalan objek. Semua strategi dari ekstraksi jalan sangat tergantung pada karakteristik data. Foto udara dan data ketinggian menjadi data pada tugas akhir ini. Mengintegrasikan foto udara dan data ketinggian akan menyelesaikan kelemahan masing-masing data dalam pengekstraksian jalan. Pada Tugas Akhir ini digunakan pendeteksian zebra cross sebagai langkah awal untuk mengenali jalan, dilanjutkan dengan threshold, region growing, dan yang terakhir, road line filtering untuk mengekstraksi jalan. Dari hasil uji coba didapatkan hasil quality pengekstraksian jalan yang dilakukan terbaik sebesar 42,2%., persentase jalan yang terdeteksi sebesar 86,5%, dan persentase objek bukan jalan yang terdeteksi sebesar 71,1% yang menggunakan metode threshold dan road line filtering. ================================================================================================================== There are some strategies in term of road extraction process such as: Image segmentation, edge detection, curve evolution, multi-resolution, artificial intelligent, line detection, road tracking, morphology operation, object recognition. All strategies of road extraction are very depend on the characteristic of the data. RGB aerial image and DSM data be the input data in this undergraduate thesis. Integrating the aerial image and elevation based data will overcome the shortcomings and weaknesses of each type of data in road extraction. In this undergraduate thesis, to recognize the road used zebra cross detection as initial step, and then used threshold, region growing, and the final step is road line filtering to extract the road. From experimental results, the quality of road extraction is 42,2%, percentage of detected road is 86,5%, and percentage of detected another object (non road) is 71,1% which used threshold and region growing method

    ROAD NETWORK EXTRACTION FROM DSM BY MATHEMATICAL MORPHOLOGY AND REASONING

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