scholarly journals PERBANDINGAN METODE KLASIFIKASI PENUTUP LAHAN BERBASIS PIKSEL DAN BERBASIS OBYEK MENGGUNAKAN DATA PiSAR-L2

2016 ◽  
Vol 13 (1) ◽  
Author(s):  
R. Johannes Manalu ◽  
Ahmad Sutanto ◽  
Bambang Trisakti
Keyword(s):  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Synthetic Aperture Radar ◽  
Spatial Resolution ◽  
Synthetic Aperture ◽  
Support Vector ◽  
Object Based ◽  
Mixed Pixels ◽  
Salt And Pepper ◽  
Neighbor Classifier

PiSAR-L2 program is an experimental program for PALSAR-2 sensor installed on ALOS-2. Research collaboration had been conducted between the Japan Aerospace Exploration Agency (JAXA) and Ministry for Research and Technology of Indonesia in 2012 to assess the ability of PiSAR-L2 data for some applications. This paper explores the utilization of PiSAR-L2 data for land cover classification in forest area using pixel-based and object-based methods, then carried out comparison between the two methods. PiSAR-L2 data full polarization with 2.1 level for Riau province was used. Field data conducted by JAXA team and landcover map from WWF were used as references to collect input and evaluation sample. Pre-processing was done by doing backscatter conversion and filtering, then classification was conducted and it`s accuracy was tested. Two methods were used, 1) Maximum Likelihood Enhance Neighbor classifier for pixel-based and 2) Support Vector Machine for object based classification. The effect of spatial resolution on classification result was also analyzed. The results show that pixel-based produced mixed pixels "salt and pepper", the classification accuracies were 62% for 2.5 m and 83% for 10 m spatial resolution. While the object-based has some advantages: high homogeneity (absence of mixed pixels), clear and sharp boundary among classes, and high accuracy (97% for 10 m spatial resolution), although it was still found errors in some classes. Abstrak Program Polarimetric Interferometric Airborne Synthetic Aperture Radar of L-band version 2 (PiSAR-L2) adalah program eksperimen sensor Phased-Array Synthetic Aperture RADAR-2 (PALSAR-2) yang dipasang pada satelit Advanced Land Observing Satellite-2 (ALOS-2). Kerjasama riset telah dilakukan antara JAXA dan Kementerian Riset dan Teknologi pada 2012 untuk mengkaji kemampuan data PiSAR L-2 yang direkam menggunakan pesawat untuk beberapa aplikasi. Kegiatan ini menggunakan data PiSAR L-2 untuk klasifikasi penutup lahan di wilayah hutan dengan metode klasifikasi berbasis piksel dan berbasis obyek, kemudian membandingkan kedua metode tersebut. Data yang digunakan adalah data PiSAR L-2 polarisasi penuh dengan level 2.1 untuk wilayah Provinsi Riau. Data lapangan diperoleh dari survei lapangan tim JAXA dan peta penutup lahan dari World Wildlife Fund  dijadikan sebagai referensi untuk sampel masukan dan pengujian. Pengolahan awal melakukan konversi backscatter dan filtering, kemudian melakukan klasifikasi dan uji akurasi. Dua metode klasifikasi yang digunakan, 1) Metode Maximum Likelihood Enhance Neighbor classifier untuk klasifikasi berbasis piksel dan 2) Metode Support Vector Machine untuk klasifikasi berbasis obyek. Pada kegiatan ini dilakukan analisis pengaruh resolusi spasial terhadap hasil klasifikasi. Hasil memperlihatkan bahwa metode berbasis piksel mempunyai piksel bercampur “salt and pepper”, akurasi klasifikasi adalah 62% untuk spasial resolusi 2.5 m dan 83% untuk spasial resolusi 10 m. Sedangkan klasifikasi berbasis obyek mempunyai kelebihan dengan homogenitas obyek yang tinggi (tidak adanya piksel bercampur), batas antara kelas yang jelas dan tegas, serta akurasi yang tinggi (97% untuk resolusi spasial 10 m), walau masih ada kesalahan pada beberapa kelas penutup lahan.

scholarly journals Mining Subsidence Prediction by Combining Support Vector Machine Regression and Interferometric Synthetic Aperture Radar Data

2020 ◽  
Vol 9 (6) ◽  
pp. 390
Author(s):  
Lichun Sui ◽  
Fei Ma ◽  
Nan Chen
Keyword(s):  
Support Vector Machine ◽  
Synthetic Aperture Radar ◽  
Loess Plateau ◽  
Mining Subsidence ◽  
Shaanxi Province ◽  
Synthetic Aperture ◽  
Support Vector ◽  
Interferometric Synthetic Aperture Radar ◽  
The Loess Plateau ◽  
Aperture Radar

Mining subsidence is time-dependent and highly nonlinear, especially in the Loess Plateau region in Northwestern China. As a consequence, and mainly in building agglomerations, the structures can be damaged severely during or after underground extraction, with risks to human life. In this paper, we propose an approach based on a combination of a differential interferometric synthetic aperture radar (DInSAR) technique and a support vector machine (SVM) regression algorithm optimized by grid search (GS-SVR) to predict mining subsidence in a timely and cost-efficient manner. We consider five Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) images encompassing the Dafosi coal mine area in Binxian and Changwu counties, Shaanxi Province. The results show that the subsidence predicted by the proposed InSAR and GS-SVR approach is consistent with the Global Positioning System (GPS) measurements. The maximum absolute errors are less than 3.1 cm and the maximum relative errors are less than 14%. The proposed approach combining DInSAR with GS-SVR technology can predict mining subsidence on the Loess Plateau of China with a high level of accuracy. This research may also help to provide disaster warnings.

scholarly journals Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images

2018 ◽  
Vol 10 (11) ◽  
pp. 1705 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Hossein Rizeei ◽  
Abdinur Abdulle
Keyword(s):  
Noise Removal ◽  
Synthetic Aperture ◽  
Support Vector ◽  
Spectral Difference ◽  
Sar Images ◽  
Thematic Map ◽  
Tropical Regions ◽  
Passive Sensor ◽  
Multiresolution Segmentation ◽  
Salt And Pepper

This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds.

scholarly journals A Comparative Study of Support Vector Machine and Maximum Likelihood Classification to Extract Land Cover of Lahore District, Punjab, Pakistan

2021 ◽  
Vol 64 (3) ◽  
pp. 265-274
Author(s):  
Fatima Mushtaq ◽  
Khalid Mahmood ◽  
Mohammad Chaudhry Hamid ◽  
Rahat Tufail
Keyword(s):  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Support Vector ◽  
Kappa Statistics ◽  
Error Matrix ◽  
Classification Techniques ◽  
Machine Learning Classification ◽  
Hyper Plane ◽  
Better Than

The advent of technological era, the scientists and researchers develop machine learning classification techniques to classify land cover accurately. Researches prove that these classification techniques perform better than previous traditional techniques. In this research main objective is to identify suitable land cover classification method to extract land cover information of Lahore district. Two supervised classification techniques i.e., Maximum Likelihood Classifier (MLC) (based on neighbourhood function) and Support Vector Machine (SVM) (based on optimal hyper-plane function) are compared by using Sentinel-2 data. For this optimization, four land cover classes have been selected. Field based training samples have been collected and prepared through a survey of the study area at four spatial levels. Accuracy for each of the classifier has been assessed using error matrix and kappa statistics. Results show that SVM performs better than MLC. Overall accuracies of SVM and MLC are 95.20% and 88.80% whereas their kappa co-efficient are 0.93 and 0.84 respectively.  

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