Cloud-Free Composite Geospatial Image Generation
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Solution Overview
Problem
Cloud cover significantly hampers satellite and aerial imagery by causing data loss and gaps, as approximately 67% of the Earth's surface is typically covered by clouds, making it difficult to monitor changes in large geographical areas over time.
Innovation Solution
A computer-implemented method and system that virtually temporally stacks satellite or aerial images of the same geographic area, using a virtual temporal projection plane to fill occluded areas in more recent images with cloud-free data from older images, ensuring the creation of composite, cloud-free images for current or historical analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If satellite or aerial imagery is used to monitor large geographical areas, then large-scale information can be gathered, but cloud cover causes data loss and gaps
Solution Approach 1:
The system performs preliminary action by capturing multiple satellite images of the same geographical area at different times before the final composite image is needed. These pre-captured images are stored and processed to create a temporal stack, allowing cloud-obscured areas in one image to be replaced with clear areas from other images taken at different times, thus preventing data loss before the final analysis occurs
Solution Approach 2:
The system merges multiple satellite images taken at different times into a single composite image. By combining information from multiple temporal snapshots, the system creates a cloud-free composite that retains the geographical coverage benefits of satellite imagery while eliminating the data loss problem caused by cloud cover in individual images
2Reliability
If multiple images are processed to compensate for cloud cover, then cloud-free composite images can be generated, but processing complexity increases
Solution Approach 1:
The processing system applies segmentation by dividing the composite image creation process into distinct steps: capturing individual satellite images, creating a temporal stack, identifying cloud-obscured areas, and replacing them with clear areas from other images. This segmented approach manages processing complexity by breaking down the complex task of cloud compensation into manageable, automated stages
Solution Approach 2:
The system uses an intermediary computational process that automatically matches and replaces cloud-obscured pixels with corresponding clear pixels from other temporal images. This intermediary processing layer handles the complexity of comparing multiple images and determining appropriate replacements, shielding the user from the underlying computational complexity while ensuring reliable cloud-free output
3Loss of time
If the most recent image is used as the base image, then the composite image is as up-to-date as possible, but cloud cover in recent images may still cause occlusions
Solution Approach 1:
The system performs preliminary action by pre-processing and storing multiple recent images in a temporal stack before composite image generation. When creating the composite, it systematically works through the stack from most recent to oldest, using the most recent clear areas available, thus preserving timeliness while compensating for cloud occlusions through预先 prepared alternative data
Solution Approach 2:
Instead of discarding recent images with cloud cover, the system inverts the traditional approach by using older images to fill gaps in recent images. The composite image is built by inverting the normal temporal flow, allowing data from older images to supplement and complete the most recent image, thereby maintaining both timeliness and completeness
Data Source
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AI summary
The present invention provides a computer implemented method and system for compensating for cloud cover to allow the build-up of composite, cloud free images of a particular geographic area. The invention takes satellite or aerial image data as an input and temporally stacks the images for a particular geographic area, with the most recent image at the top. The time for the composite image to be produced (may be the present time, or may be a time from the past) is defined as a temporal projection plane. The most recent image prior to this plane is analysed to identify any areas obscured by clouds. Cloud free areas of the most recent image are projected onto the temporal projection plane. For the identified obscured areas, older, cloud free images in the stack are projected onto the temporal projection plane to fill the obscured areas. This forms a cloud free composite image at the defined temporal projection plane.