LULC-Guided SAR-to-Optical Visualization With GAN Translation
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Solution Overview
Problem
SAR images are challenging to interpret due to geometric distortions, speckle noise, and lack of color information, making them uninterpretable by untrained individuals, while conventional image-to-image translation methods like CycleGAN and pix2pix fail to preserve land cover information or produce blurry results.
Innovation Solution
A method and system using a Generative Adversarial Network (GAN) with two generators and a discriminator, guided by Land Use Land Cover (LULC) data, to translate SAR images into optical images, incorporating LULC maps to provide semantic information and improve visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If SAR images are used for remote sensing tasks, then images can be collected during night and penetrate through clouds, but the images have geometric distortions, speckle noise, and lack color information making them uninterpretable by untrained people
Solution Approach 1:
The patent introduces an intermediary system (GAN-based translation model) that converts SAR images into optical-like images. This intermediary transformation preserves the advantages of SAR imaging while making the output interpretable by untrained users through familiar visual characteristics
Solution Approach 2:
The patent applies colorization techniques to SAR images by translating them into RGB optical images with realistic colors and textures. This color transformation makes the images visually appealing and interpretable while retaining the underlying structural information from SAR data
2Ease of operation
If conventional image-to-image translation methods like CycleGAN and pix2pix are used, then SAR images can be translated into optical images, but land cover information is not preserved or results are blurry
Solution Approach 1:
The patent incorporates feedback mechanisms where the translated optical images are evaluated against ground truth data, and the translation model is iteratively improved. This feedback loop ensures that land cover information is preserved while achieving realistic visual output
Solution Approach 2:
The patent performs preliminary processing steps including data augmentation, normalization, and feature extraction before translation. These preliminary actions prepare the SAR images in a way that preserves critical land cover information throughout the translation process
3Ease of operation
If pseudo colorization techniques are used on SAR images, then colors can be assigned to encode pixels, but the images remain substantially different from optical remote sensing images
Solution Approach 1:
The patent replaces traditional pseudo-colorization mechanics with a deep learning-based translation system. Instead of manually assigning colors based on intensity values, the system learns the complex mapping between SAR and optical image domains, producing results that closely resemble actual optical images
Data Source
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AI summary
Optical images in remote sensing are contaminated by cloud cover and bad weather conditions and are only available during the daytime. Whereas SAR images are completely cloud free, independent of weather conditions and can be acquired both during the day and at night. However, due to the speckle effect and side looking imaging mechanism of SAR images, they are not easily interpretable by untrained people. To address this issue, the present disclosure provides a method and system for LLTLC guided SAR visualization, wherein a GAN is trained to translate SAR images to optical images for visualization. A given SAR image is fed into a first generator of the GAN to obtain LLTLC map which is then concatenated with the SAR image and fed into a second generator of the GAN to generate an optical image. The LULC map provides semantic information required for generation of more realistic optical image.