Deep learning for remote sensing images and their interpretation

• Main Author: Meraoumia, Ines
• Other Authors: Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom Paris (IMT)-Télécom Paris, Institut Polytechnique de Paris, Florence Tupin, Loïc Denis
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: HAL CCSD 2023
• Subjects: Sar; Deep learning; Remote Sensing; Apprentissage profond; Imagerie Satellitaire; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]; Sea ice
• Online Access: https://theses.hal.science/tel-04399430; https://theses.hal.science/tel-04399430/document; https://theses.hal.science/tel-04399430/file/130626_MERAOUMIA_2023_archivage.pdf

Synthetic Aperture Radar (SAR) images are not affected by the presence of clouds or variations of sunlight. They provide very useful information for Earth observation (chapter 1).They are impacted by strong fluctuations called "speckle" which make their interpretation difficult. The speckle is a phenomenon intrinsic to the coherent illumination of the scene by the radar, meaning that speckle-free images can not be captured and used as reference to train models.The properties of speckle are different from that of the traditional additive white Gaussian noise used to model corruptions in optical images, and proper despeckling algorithms are needed. Most of them rely on statistics derived from the Goodman's model (chapter 2). Recently, deep learning based methods have been very successful at despeckling a single SAR image. This work focuses on improving the despeckling performance by jointly processing several input images to exploit the common information while still preventing the propagation of differences from one image to another (chapter 3).The despeckling of Sentinel-1 GRDM Extra Wide images of sea ice is studied in Chapter 4 for sea ice studies. The ice is shifting quickly on the sea and multi-temporal stacks of a specific area can not be used for despeckling purposes due to structural changes. In the images, thermal noise can not be neglected because the reflectivity values of water and ice are very low and close to the thermal noise floor. We propose a dual-polarimetric despeckling framework where HH and HV polarimetric channels are used as input and are jointly despeckled in a single pass. The network is trained in a self-supervised way inspired by the existing SAR2SAR framework and takes corrected images where the thermal noise floor level has been removed as input. Our approach shows a clear improvement over existing image restoration techniques on Sentinel-1 images of the Artic.Despeckling can be improved by combining measurements pertaining to common information within the temporal stack while ...