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Comparison of SeaWinds Backscatter Imaging Algorithms.

David G Long
Published in: IEEE journal of selected topics in applied earth observations and remote sensing (2016)
This paper compares the performance and tradeoffs of various backscatter imaging algorithms for the SeaWinds scatterometer when multiple passes over a target are available. Reconstruction methods are compared with conventional gridding algorithms. In particular, the performance and tradeoffs in conventional 'drop in the bucket' (DIB) gridding at the intrinsic sensor resolution are compared to high-spatial-resolution imaging algorithms such as fine-resolution DIB and the scatterometer image reconstruction (SIR) that generate enhanced-resolution backscatter images. Various options for each algorithm are explored, including considering both linear and dB computation. The effects of sampling density and reconstruction quality versus time are explored. Both simulated and actual data results are considered. The results demonstrate the effectiveness of high-resolution reconstruction using SIR as well as its limitations and the limitations of DIB and fDIB.
Keyphrases
  • high resolution
  • deep learning
  • machine learning
  • single molecule
  • artificial intelligence
  • convolutional neural network
  • randomized controlled trial
  • big data