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Segmentation and inpainting of sensor failures in remote sensing images

Grant number: 19/24259-0
Support type:Scholarships abroad - Research Internship - Scientific Initiation
Effective date (Start): December 15, 2019
Effective date (End): March 14, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal researcher:Wallace Correa de Oliveira Casaca
Grantee:Dayara Pereira Basso
Supervisor abroad: Pedro Miguel Berardo Duarte Pina
Home Institution: Universidade Estadual Paulista (UNESP). Campus de Rosana. Rosana , SP, Brazil
Research place: Instituto Superior Técnico (IST), Portugal  
Associated to the scholarship:18/06756-3 - Development of an unsupervised inpainting methodology for occlusion removal in urban images, BP.IC


Although Remote Sensing (RS) images have been successfully used in several applications, the quality of the processed images may be dramatically reduced due to particular issues occurred in the data acquisition step, such as changes in signal capture or sensor failures, leading to the emergence of occlusions within the images. These occlusions make post-processing tasks less effective in practice, which includes image classification applications or the analysis of the original data which has been damaged. Therefore, this project focuses on restoring RS images containing failures from capturing sensors, specifically the Landsat-7 sensor. The proposed solution combines the prior segmentation of the occluded targets, as it was specially designed to deal with degenerations caused by the Landsat-7, and the inpainting of segmented failures, which will be performed by exploiting two digital inpainting approaches: the first one based on PDEs, and the another one relying on patch replication. As a result, it is expected that the detected occlusions, i.e., the regions that do not correspond to the original image scenario, will be fully inpainted by the computational solutions to be implemented in this project. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
BASSO, DAYARA; COLNAGO, MARILAINE; AZEVEDO, SAMARA; SILVA, ERIVALDO; PINA, PEDRO; CASACA, WALLACE. Combining morphological filtering, anisotropic diffusion and block-based data replication for automatically detecting and recovering unscanned gaps in remote sensing images. EARTH SCIENCE INFORMATICS, APR 2021. Web of Science Citations: 0.

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