scholarly journals 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)

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Galdita Aruba Chulafak ◽  
Dony Kushardono ◽  
NFN Zylshal
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Synthetic Aperture Radar ◽  
Window Size ◽  
Synthetic Aperture ◽  
Gray Level ◽  
Dual Polarization ◽  
Texture Information ◽  
Sar Data ◽  
Aperture Radar

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. AbstrakPada 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.

Mapping the 2010 Merapi pyroclastic deposits using dual-polarization Synthetic Aperture Radar (SAR) data

2015 ◽  
Vol 158 ◽  
pp. 180-192 ◽  
Author(s):  
Akhmad Solikhin ◽  
Virginie Pinel ◽  
Jean Vandemeulebrouck ◽  
Jean-Claude Thouret ◽  
Muhamad Hendrasto
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Dual Polarization ◽  
Pyroclastic Deposits ◽  
Sar Data ◽  
Aperture Radar

scholarly journals Use of synthetic aperture radar for recognition of Coastal Geomorphological Features, land-use assessment and shoreline changes in Bragança coast, Pará, Northern Brazil

2003 ◽  
Vol 75 (3) ◽  
pp. 341-356 ◽  
Author(s):  
Pedro W. M. Souza-Filho ◽  
Waldir R. Paradella
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Shoreline Changes ◽  
Geomorphological Mapping ◽  
Northern Brazil ◽  
Coastal Changes ◽  
Sar Data ◽  
Aperture Radar

Synthetic Aperture Radar (SAR) images are being used more extensively than ever before for geoscience applications in the moist tropics. In this investigation, a RADARSAT1-1 C-HH SAR image acquired in 1998 was used for coastal mapping and land-cover assessment in the Bragança area, in the northern Brazil. The airborne GEMS 1000 X-HH radar image acquired in 1972 during the RADAM Project was also used for evaluating coastal changes occurring over the last three decades. The research has confirmed the usefulness of RADARSAT-1 image for geomorphological mapping and land-cover assessment, particularly in macrotidal mangrove coasts. It was possible to map mangroves, salt marshes, chenier sand ridges, dunes, barrier-beach ridges, shallow water morphologies and different forms of land-use. Furthermore, a new method to estimate shoreline changes based on the superimposition of vectors extracted from both sources of SAR data has indicated that the shoreline has been subjected to severe coastal erosion responsible for retreat of 32 km² and accretion of 20 km², resulting in a mangrove land loss of almost 12 km². In an application perspective, orbital and airborne SAR data proved to be a fundamental source of information for both geomorphological mapping and monitoring coastal changes in moist tropical environments.

scholarly journals A Framework for Correcting Ionospheric Artifacts and Atmospheric Effects to Generate High Accuracy InSAR DEM

2020 ◽  
Vol 12 (2) ◽  
pp. 318 ◽  
Author(s):  
Zhiwei Liu ◽  
Cui Zhou ◽  
Haiqiang Fu ◽  
Jianjun Zhu ◽  
Tingying Zuo
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Atmospheric Effects ◽  
Interferometric Synthetic Aperture Radar ◽  
Dual Polarization ◽  
Digital Elevation ◽  
Split Spectrum ◽  
Dem Accuracy ◽  
Study Sites ◽  
Aperture Radar

Repeat-pass interferometric synthetic aperture radar is a well-established technology for generating digital elevation models (DEMs). However, the interferogram usually has ionospheric and atmospheric effects, which reduces the DEM accuracy. In this paper, by introducing dual-polarization interferograms, a new approach is proposed to mitigate the ionospheric and atmospheric errors of the interferometric synthetic aperture radar (InSAR) data. The proposed method consists of two parts. First, the range split-spectrum method is applied to compensate for the ionospheric artifacts. Then, a multiresolution correlation analysis between dual-polarization InSAR interferograms is employed to remove the identical atmospheric phases, since the atmospheric delay is independent of SAR polarizations. The corrected interferogram can be used for DEM extraction. Validation experiments, using the ALOS-1 PALSAR interferometric pairs covering the study areas in Hawaii and Lebanon of the U.S.A., show that the proposed method can effectively reduce the ionospheric artifacts and atmospheric effects, and improve the accuracy of the InSAR-derived DEMs by 64.9% and 31.7% for the study sites in Hawaii and Lebanon of the U.S.A., respectively, compared with traditional correction methods. In addition, the assessment of the resulting DEMs also includes comparisons with the high-precision Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) altimetry data. The results show that the selection of reference data will not affect the validation results.

scholarly journals Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qingyan Meng ◽  
Linlin Zhang ◽  
Qiuxia Xie ◽  
Shun Yao ◽  
Xu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Landsat 8 ◽  
Combined Use ◽  
Artificial Neural ◽  
Agricultural Areas ◽  
Aperture Radar

Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

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