Assessment of the X- and C-Band Polarimetric SAR Data for Plastic-Mulched Farmland Classification
| العنوان: | Assessment of the X- and C-Band Polarimetric SAR Data for Plastic-Mulched Farmland Classification |
|---|---|
| المؤلفون: | Dandan Li, Chang-An Liu, Zhongxin Chen, Di Wang |
| المصدر: | Remote Sensing, Vol 11, Iss 6, p 660 (2019) Remote Sensing; Volume 11; Issue 6; Pages: 660 |
| بيانات النشر: | MDPI AG, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Synthetic aperture radar, 010504 meteorology & atmospheric sciences, C band, Science, 0211 other engineering and technologies, Polarimetry, 02 engineering and technology, Land cover, 01 natural sciences, law.invention, law, Agricultural land, TerraSAR-X, plastic-mulched farmland (PMF), classification, agriculture, polarimetric decomposition, Radar, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Land use, Random forest, General Earth and Planetary Sciences, Environmental science |
| الوصف: | We present a classification of plastic-mulched farmland (PMF) and other land cover types using full polarimetric RADARSAT-2 data and dual polarimetric (HH, VV) TerraSAR-X data, acquired from a test site in Hebei, China, where the main land covers include PMF, bare soil, winter wheat, urban areas and water. The main objectives were to evaluate the outcome of using high-resolution TerraSAR-X data for classifying PMF and other land covers and to compare classification accuracies based on different synthetic aperture radar bands and polarization parameters. Initially, different polarimetric indices were calculated, while polarimetric decomposition methods were used to obtain the polarimetric decomposition components. Using these polarimetric components as input, the random forest supervised classification algorithm was applied in the classification experiments. Our results show that in this study full-polarimetric RADARSAT-2 data produced the most accurate overall classification (94.81%), indicating that full polarization is vital to distinguishing PMF from other land cover types. Dual polarimetric data had similar levels of classification error for PMF and bare soil, yielding mapping accuracies of 53.28% and 59.48% (TerraSAR-X), and 59.56% and 57.1% (RADARSAT-2), respectively. We found that Shannon entropy made the greatest contribution to accuracy in all three experiments, suggesting that it has great potential to improve agricultural land use classifications based on remote sensing. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2072-4292 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e0b49f13b8e0429e0a3bf807d113deda http://www.mdpi.com/2072-4292/11/6/660 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....e0b49f13b8e0429e0a3bf807d113deda |
| قاعدة البيانات: | OpenAIRE |
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