期刊
REMOTE SENSING
卷 14, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/rs14040962
关键词
ionospheric scintillation; ALOS; PALSAR-2; FFT filtering; SNAP; ENVI
类别
资金
- Edson E. Sano (Embrapa Cerrados)
- Japan Aerospace Exploration Agency (JAXA) under ALOS Research Announcement [1090]
This article presents a methodology using Fourier fast transform to filter stripes on radar images caused by ionospheric disturbances. The filtered images were then classified using random forest, showing improved classification performance compared to the original scenes.
The monitoring of forest degradation in the Amazon through radar remote sensing methodologies has increased intensely in recent years. Synthetic aperture radar (SAR) sensors that operate in L-band have an interesting response for land use and land cover (LULC) as well as for aboveground biomass (AGB). Depending on the magnetic and solar activities and seasonality, plasma bubbles in the ionosphere appear in the equatorial and tropical regions; these factors can cause stripes across SAR images, which disturb the interpretation and the classification. Our article shows a methodology to filter these stripes using Fourier fast transform (FFT), in which a stop-band filter removes this noise. In order to make this possible, we used Environment for Visualizing Images (ENVI), Sentinel Application Platform (SNAP), and Interactive Data Language (IDL). The final filtered scenes were classified by random forest (RF), and the results of this classification showed superior performance compared to the original scenes, showing this methodology can help to recover historic series of L-band images.
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