Cloud Detection in Single Panchromatic Images Using Feature-Space Thresholds
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Solution Overview
Problem
Current cloud detection methods in digital images, particularly in satellite imagery, are inadequate for accurate automated filtering, often requiring human intervention and relying on multiple images or complex processing techniques like stereo imaging and fractal dimensions, which are not always feasible or efficient.
Innovation Solution
A computer-implemented method for identifying clouds in a single panchromatic or monochromatic digital image using homogeneity and brightness features, without the need for stereo imaging, different angles, wavelengths, or thermal data, by applying feature-space transformations and thresholds to distinguish cloud pixels from background pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cloud detection methods are used, then cloud detection capability is provided, but accuracy is insufficient and human intervention is required
Solution Approach 1:
The patent transforms the input image into a new feature space using mathematical transformations (e.g., logarithmic transformation of normalized difference cloud index). This changes the parameter representation of cloud features, making them more distinguishable from background features in the transformed space, thereby improving detection accuracy and enabling automated filtering
Solution Approach 2:
The patent replaces manual human inspection and filtering with an automated computer-implemented method. The system uses algorithmic processing of image features, automatic threshold determination, and computational classification to substitute the mechanical human operation, achieving both high accuracy and full automation
2Measurement precision
If multiple images or complex processing techniques are used, then cloud detection capability is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential cloud detection features (brightness and homogeneity) from the image data, transforming them into a simplified feature space. This extraction approach avoids the need for complex stereo imaging, multiple wavelengths, or thermal data, reducing device complexity while maintaining detection accuracy through focused feature analysis
3Productivity
If traditional cloud detection methods are used, then cloud detection is provided, but processing efficiency is low and resource-intensive
Solution Approach 1:
The patent applies mathematical transformations to convert raw image parameters into a optimized feature space where cloud detection is more efficient. The transformation of normalized difference cloud index using logarithmic functions and the creation of new feature combinations streamline the detection process, reducing computational iterations and resource consumption while improving processing speed
Data Source
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AI summary
A computer-implemented method for identifying clouds in a digital image, comprising pixels, of a scene, the method comprising quantifying pixel-level characteristic/s in each of a multiplicity of pixels within a digital image of a scene; comparing function/s of the pixel-level characteristic/s to threshold/s thereby to generate comparison result/s; and using a controller for generating an output identifying clouds in the digital image, including identifying presence of cloudiness at at least one first pixel in the digital image, at least partly because the at least one comparison result indicates that the first pixel falls below the threshold/s, and identifying absence of cloudiness at at least one second pixel in the digital image, at least partly because the at least one comparison result indicates that the second pixel exceeds the threshold/s.