EO Satellite Image Dehazing and Super-Resolution for Object Detection
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Solution Overview
Problem
Current object monitoring, detection, and segmentation systems face challenges such as labor-intensive dataset annotation, resource-intensive computational requirements, and performance degradation in low-light or occluded conditions, particularly in electro-optical imagery, which affects precision and interpretability.
Innovation Solution
A system and method utilizing an image processing engine for dehazing, resolution enhancement, and geospatial alignment of electro-optical satellite imagery, followed by machine learning-based object detection and segmentation using trained models, specifically employing generative adversarial networks for dehazing and super-resolution techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If state-of-the-art deep learning models are used for object detection and segmentation, then accuracy and speed are improved, but computational resources and complexity increase substantially
Solution Approach 1:
The patent applies preliminary image enhancement actions (dehazing, super-resolution, contrast adjustment) before object detection to improve input image quality. This preprocessing prepares the imagery in advance to make subsequent detection more accurate while potentially allowing use of less computationally intensive models, as the enhancement steps address quality issues before they propagate through the detection pipeline.
2Measurement precision
If advanced image processing and deep learning models are deployed, then object detection precision is improved, but resource consumption increases
Solution Approach 1:
The system performs preliminary image enhancement (dehazing, super-resolution) before detection to improve input quality. This allows downstream detection models to operate on pre-enhanced images that require less computational processing to achieve accurate results, effectively shifting computational effort to more efficient preprocessing operations.
Solution Approach 2:
The patent replaces traditional mechanical/image processing methods with AI-based approaches (GANs for dehazing and super-resolution, deep learning for detection). These AI-based systems provide more efficient and adaptive processing compared to conventional methods, achieving better precision with optimized resource utilization through learned patterns rather than exhaustive computation.
3Reliability
If EO imagery is processed without enhancement, then processing speed is maintained, but image quality and detection reliability deteriorate in hazy or low-light conditions
Solution Approach 1:
The system applies preliminary enhancement actions (dehazing, super-resolution, contrast adjustment) to EO imagery before object detection. This preprocessing improves image quality in challenging conditions (haze, low-light) to ensure reliable detection results, while the modular architecture keeps processing complexity manageable through specialized enhancement modules.
Data Source
AI summary
A system and method for object monitoring, detection, and segmentation in electro-optical (EO) satellite imagery data comprising an image processing engine configured to prepare EO data for analysis by an inference engine which utilizes one or more trained models to perform object detection or image segmentation. The workflow begins with ingesting satellite data, followed by de-hazing to remove atmospheric interference. Image enhancement improves resolution and geo-registration ensures precise spatial alignment. The processed image is then fed into a machine learning-based object detection or image segmentation network, trained to identify specific objects of interest. This integrated approach leverages advanced technologies to extract actionable insights from satellite data, enabling efficient and precise object monitoring, detection, and segmentation.