Geographical Line Image Encoding With Multi-Resolution Compression
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Solution Overview
Problem
Existing methods for processing satellite imagery of geographical lines, such as roads and railways, face challenges in efficiently encoding and compressing large amounts of image data while maintaining the ability to accurately monitor conditions, leading to burdensome processing times and resource requirements, especially in rural areas with sparse transport networks.
Innovation Solution
A method involving data compression that reduces spatial resolution non-uniformly across an image, maintaining high accuracy for features near the geographical line and allowing higher compression elsewhere, combined with geometric transformation into a local internal frame of reference for enhanced analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform high-resolution encoding is applied to the entire image, then monitoring accuracy is improved, but data storage and transmission requirements increase significantly
Solution Approach 1:
The patent applies different spatial resolutions to different regions of the image based on their importance. Regions containing the geographical line and its immediate surroundings are encoded at high resolution to maintain monitoring accuracy, while peripheral regions are encoded at lower resolution to reduce data volume. This local differentiation resolves the contradiction by optimizing the balance between accuracy and data efficiency.
Solution Approach 2:
The image is divided into multiple zones with different encoding requirements: a central high-priority zone containing the geographical line encoded at high resolution, and peripheral low-priority zones encoded at lower resolution. This segmentation allows the system to allocate computational and storage resources efficiently while maintaining necessary monitoring accuracy.
2Measurement precision
If high-resolution encoding is applied to the entire image, then feature detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent encodes only the critical regions containing geographical features at high resolution, while encoding peripheral regions at lower resolution. This approach maintains feature detection accuracy for important elements while significantly reducing the total computational burden and processing time required for the entire image.
Solution Approach 2:
The patent applies high-resolution encoding selectively only where necessary (partial action) rather than uniformly across the entire image. This partial application of high-resolution processing maintains sufficient feature detection accuracy while avoiding the excessive processing time that would result from full-image high-resolution encoding.
3Reliability
If comprehensive image data is collected from wide areas, then monitoring completeness is improved, but computational resources required increase
Solution Approach 1:
The patent collects and processes image data at different quality levels for different regions. High-quality data is collected for areas containing the geographical line and its surroundings to ensure monitoring completeness, while lower-quality data suffices for peripheral areas, thereby reducing overall computational resource requirements.
Data Source
AI summary
The invention relates to a method of encoding image data representing a geographical line, GL, or its surroundings, and a data processing system being configured to perform the encoding. The method comprises applying a data compression process to reduce the spatial resolution of a given image represented by the image data. Specifically, the image may form an imagery layer of an imagery stack comprising various imagery layers, each imagery layer representing an individual image, e.g. of a same geographical region. The data compression process comprises: generating two or more sublayers of the image by resampling at least one region of the image at a respective lower spatial resolution than its original spatial resolution, while maintaining or resampling the spatial resolution of at least one other region of the image such that, when the sublayers are overlapped to jointly define a combined multi-resolution representation of the image, the average resolution of the combined multi-resolution representation is lower than the original resolution of the image.