Multi-Resolution Satellite Image Change Detection
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Solution Overview
Problem
Current satellite image analysis systems are unable to process images of different resolutions or spatial frequencies and do not offer users the ability to select specific filtering and masking techniques for detecting changes over time in satellite images.
Innovation Solution
The system employs computer vision techniques, supervised and unsupervised machine learning, and deep neural network models to automatically analyze multi-spectral, multi-resolution satellite images, detecting changes and generating alerts through a monitoring and alerting system, with inputs and outputs provided via an analytics web interface or API.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional satellite image analysis systems process images, then basic image analysis is performed, but they cannot handle images of different resolutions or spatial frequencies
Solution Approach 1:
The patent segments the satellite image processing task into multiple resolution levels using a pyramid structure. Images are divided into coarse and fine resolution layers, allowing the system to process different spatial frequencies separately. This segmentation enables multi-resolution analysis while managing system complexity through hierarchical organization of processing stages.
Solution Approach 2:
The patent introduces a resolution dimension by transforming single-resolution images into multi-resolution pyramid structures. This dimensional transformation allows the system to analyze images at multiple spatial scales simultaneously, adding the ability to handle varying resolutions without proportionally increasing overall system complexity.
2Extent of automation
If automated change detection is implemented, then change detection capability is improved, but user control over filtering and masking techniques is reduced
Solution Approach 1:
The patent implements dynamic control mechanisms that allow users to adjust filtering and masking parameters in real-time based on detection results. The system transitions from static automated processing to dynamic semi-automated operation, where users can modify processing parameters after initial automated detection, balancing automation efficiency with user control flexibility.
3Measurement precision
If multi-spectral multi-resolution analysis is performed, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing by creating resolution pyramids and applying spectral transformations before actual change detection. This preliminary organization of data at multiple resolutions allows subsequent change detection operations to be more efficient, reducing overall processing time while maintaining high detection accuracy through pre-computed multi-scale representations.
Solution Approach 2:
The patent creates simplified copies of satellite images at different resolution levels within the pyramid structure. These copied representations at coarse resolutions enable rapid preliminary analysis, while finer resolution copies are used only where needed for detailed detection, reducing total processing time compared to analyzing only full-resolution images.
Data Source
AI summary
The systems, methods, and computer program products disclosed herein pertain to image analysis techniques that may exploit computer vision, supervised and unsupervised machine learning, and deep neural network models in order to detect change and determine spatio-temporal attention regions of relevant change in multi-spectral and multi-resolution satellite images. In some embodiments, the automatic detection of changes in regions over large scales of satellite imagery data may be further used to generate alerts in the context of a monitoring and alerting system. In some embodiments, the input to the system may be provided through an analytics web interface or an application programming interface (API), and the output may also be served through the web interface or API.