Scene Change Detection via Multi-Sensor Object Classification
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Solution Overview
Problem
Existing methods for detecting object-level changes in a scene, such as new construction or object movement, are inadequate due to reliance on pixel-based analysis and spectral image comparisons, which fail to accurately identify changes and are hindered by differing capture conditions and sensor incompatibilities.
Innovation Solution
A method using multiple sensors to classify pixel groups in reference images into object types, developing classification rules through deep learning, and applying these rules to unclassified images to determine changes, regardless of capture conditions or sensor compatibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-based analysis is used to detect changes in a scene, then the analysis process is simple, but it fails to identify object-level changes and determine their specific nature
Solution Approach 1:
The patent segments images into pixel groups that represent objects, transitioning from individual pixel analysis to object-level analysis. This segmentation enables the system to identify and characterize specific objects (e.g., houses, cars) while maintaining computational feasibility through automated clustering algorithms.
Solution Approach 2:
The patent introduces pixel groups as an intermediary between individual pixels and complete objects. These pixel groups serve as intermediate representations that capture object-level information while enabling systematic comparison across multiple images through classification rules.
2Adaptability or versatility
If spectral images captured by different sensors are used for comparison, then more data sources are available, but the images are incompatible for pixel-by-pixel comparison
Solution Approach 1:
The patent creates a universal classification framework that works across different sensor types (RGB, infrared, etc.). By developing sensor-specific classification rules that all map to common object categories, the system enables comparison of images from diverse sensors without requiring identical sensor characteristics.
Solution Approach 2:
The patent transforms images from different sensor modalities into a common parameter space through classification. Instead of comparing raw pixel values or spectral signatures directly, the system converts all images into classified pixel groups representing object types, enabling meaningful comparison across sensor types.
3Adaptability or versatility
If images captured under different conditions are used for comparison, then more environmental coverage is achieved, but illumination and cloud cover differences complicate analysis
Solution Approach 1:
The patent performs preliminary classification of pixel groups into object types before comparing images. By establishing classification rules in advance for each sensor type and applying them to all images regardless of capture conditions, the system prepares the data in a standardized format that eliminates the confounding effects of varying illumination and weather conditions.
Data Source
AI summary
An example method includes determining first classification rule(s) based on first data that classifies pixel groups of first reference image(s) as types of objects. The first reference image(s) are captured by a first sensor. The method further includes determining second classification rule(s) based on second data that classifies pixel groups of second reference image(s) as types of objects. The second reference image(s) are captured by a second sensor. The method includes classifying, as respective first objects, pixel group(s) of a first image of a scene based on the first classification rule(s). The method includes classifying, as respective second objects, pixel group(s) of a second image of the scene based on the second classification rule(s). The method further includes determining whether a change occurred in the scene based on the object classification of the first image(s) and the second image(s).