Satellite Cloud Detection Using Offset Sensor Parallax
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Solution Overview
Problem
Existing methods for detecting clouds in satellite imagery using physically offset sensor arrays struggle to accurately identify cloud presence due to differences in satellite viewing angles and temporal offsets between the sensor arrays, leading to inconsistencies in image data.
Innovation Solution
A method and system that combines movement mask data and cloud mask data, produced from dissimilarities and spectral information in image data from offset sensor arrays, to generate cloud detection data, leveraging the physical offset between the arrays to enhance cloud detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud detection is performed using a single sensor array, then the detection process is simple, but detection accuracy is reduced due to inability to account for parallax and temporal offsets
Solution Approach 1:
The satellite is divided into multiple sensor arrays (first sensor array and second sensor array) positioned at different physical locations. Each sensor array independently captures image data from the same geographic region at different times, enabling parallax-based cloud detection through comparison of the captured images
Solution Approach 2:
A processor serves as an intermediary that receives image data from both sensor arrays, generates movement mask data based on dissimilarities between the images, generates cloud mask data based on spectral information, and produces cloud detection data by intersecting the two masks. This intermediary processing system resolves the contradiction by systematically handling the complexity of multi-sensor data integration
2Measurement precision
If multiple sensor arrays with physical offset are used, then cloud detection accuracy is improved through parallax measurement, but data processing complexity increases due to temporal offsets and dissimilarity calculation
Solution Approach 1:
The system performs preliminary actions by generating movement mask data from dissimilarities between images from different sensor arrays before final cloud detection. This preliminary processing step pre-computes the parallax effects and temporal offset compensations, making the subsequent cloud detection process more manageable and accurate
Solution Approach 2:
The system adds a temporal dimension to the detection process by capturing images at different times (T1 and T2) with a temporal offset. This temporal dimension, combined with the spatial offset between sensor arrays, creates a four-dimensional detection space (x, y, time, spectral) that enables more accurate cloud detection through multi-dimensional data intersection
3Reliability
If image data from different times is used, then temporal offset provides additional cloud movement information, but inconsistencies in image data increase due to temporal offset
Solution Approach 1:
The system converts the harmful effect of temporal offsets (which cause image data inconsistencies) into a beneficial feature. By deliberately using images captured at different times with known temporal offsets, the system can track cloud movement and improve detection reliability. The processor uses the temporal offset as additional information rather than treating it as a source of error
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution effectively identifies cloud locations by intersecting movement and cloud mask data, improving cloud detection accuracy and reliability in satellite imagery by accounting for parallax and temporal offsets between the sensor arrays.
Implementation Method 1
receiving first and second image data obtained, respectively, using the first and second sensor arrays carried by the satellite
Implementation Method 2
a second satellite viewing angle associated with the second image data obtained using the second sensor array differs from a first satellite viewing angle associated with the first image data obtained using the first sensor array, with a difference between the first and second satellite viewing angles being a parallax angle
Data Source
AI summary
Described herein are methods and systems for detecting clouds in satellite imagery captured using first and second sensor arrays that are carried by a satellite and physically offset from one another on the satellite. Movement mask data is produced based first image data and the second image data, obtained, respectively, using the first and second sensor arrays carried by the satellite. Cloud mask data is produced based on spectral information included in one of the first and second image data. Cloud detection data is produced based on the movement mask data and the cloud mask data, the cloud detection data indicating where it is likely, based on both the movement mask data and the cloud mask data, that one or more clouds are represented within one of the first and second image data. The cloud detection data can be used in various ways to account for the clouds included within the satellite imagery.