2023 article
NOVEL DEEP LEARNING FRAMEWORK FOR IMPUTING HOLES IN ORTHORECTIFIED VHR IMAGES
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, pp. 5158–5161.
TL;DR:
A new deep learning architecture based on Wide Activation Super Resolution (WDSR) network combined with an Adaptive Instance Normalization (AdaIN) based style transfer for imputing holes (missing pixels) in orthorectified images is presented.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(OpenAlex)
Source: Web Of Science
Added: March 25, 2024
Many downstream applications, such as agricultural monitoring, damage assessments, and urban planning, have benefited from remote sensing imagery. Ortho image generation from very high-resolution images has unpleasant side effects in places of building occlusions leaving holes in the orthorectified images. As a result, blank pixels caused by orthorectification must be filled in prior to downstream tasks such as machine learning. In this paper, we present a new deep learning architecture based on Wide Activation Super Resolution (WDSR) network combined with an Adaptive Instance Normalization (AdaIN) based style transfer for imputing holes (missing pixels) in orthorectified images. To test and validate the performance of imputation algorithms, we developed a new multi-resolution benchmark dataset consisting of WorldView-3 and Sentinel-2 images. Our experiments show that the WDSR framework outperforms other state-of-the-art (SOTA) deep learning methods and the Ordinary Kriging method. Our proposed method has improved the mean squared error (MSE) by at least 12.54% in comparison to existing SOTA methods.
