Convolutional neural networks for satellite remote sensing at coarse resolution. Application for the SST retrieval using IASI

[摘要] Traditional Neural Networks (NN) have been popular in the satellite remote sensing community for the last 25 years. For coarse resolution infrared or microwave instruments, NN algorithms have been used at the pixel level. New neural architectures such as Convolutional Neural Networks (CNN) use the Deep Learning (DL) approach to solve complex problems at the image scale. For instance, CNNs have been applied to high resolution instruments (SAR or in the visible domain) to detect surface waters or vegetation. High resolution data is better suited for image processing techniques because spatial features are stronger and pixel noise can be an important issue. CNNs applications are generally related to image classification or segmentation, less for regression problems dealing with the estimation of a variable in each pixel of the image. The objective of this paper is to better understand how and on which conditions CNNs work, and how beneficial they can be for coarse resolution instruments such as IASI (Infrared Atmospheric Sounding Interferometer). The CNN and DL approaches are tested in a regression mode, to estimate the Sea Surface Temperature (SST) at the image scale. The CNN technique is compared to a traditional pixel-based NN: both have a SST retrieval error of 0.3 K. An instrument noise and a missing data sensitivity studies are conducted. It is shown that the use of the CNN approach in this simpleexperiment context is beneficial only under some conditions: when the variable to retrieve has enough spatial coherency (simple smoothness or presence of spatial features in the images), and when the instrument noise at the pixel scale is larger than a threshold. This study is a preliminary illustration of what can be expected from CNNs for coarse resolution instruments such as IASI.

[发布日期] 2021-09-15

[关键词] Satellite remote sensing;Convolutional neural networks;IASI;Sea surface temperature;Deep learning