Multispectral Landscape Mapping via Segmented Camera Rig
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current remote sensing technologies face challenges in efficiently generating high-resolution geo-referenced spectral imagery of landscapes, particularly in monitoring vegetative health and detecting environmental issues over large areas.
Innovation Solution
The implementation of a multispectral three-dimensional mapping apparatus, comprising a camera rig with a wide-field of view (WFOV) camera and at least one multispectral (MS) camera, mounted on an aerial platform. This setup allows for sequential capture of WFOV and NFOV MS image data with partial overlap, enabling efficient geo-referencing and orthorectification of images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a narrow-field of view (NFOV) multispectral camera is used to capture high-resolution spectral imagery, then measurement precision is improved, but the area of landscape that can be monitored decreases and flight time increases
Solution Approach 1:
The system segments the imaging function into two specialized cameras: a WFOV camera for capturing broad landscape coverage and an NFOV MS camera for capturing high-resolution spectral data of specific regions. This segmentation allows each camera to optimize its function, resolving the contradiction between coverage area and spectral resolution.
Solution Approach 2:
The system adds the temporal dimension by sequentially capturing images at multiple positions along the flight path. The WFOV camera captures broad coverage while the NFOV MS camera captures detailed spectral data, and these are combined through image stitching to create high-resolution multispectral mosaics of large areas, effectively adding a time dimension to resolve the area-resolution tradeoff.
2Reliability
If extensive overlap in image capture is implemented to ensure complete landscape coverage, then reliability of landscape monitoring is improved, but productivity decreases due to increased flight time and data processing
Solution Approach 1:
The system uses partial overlap between adjacent WFOV images and between WFOV and NFOV images, which is sufficient for reliable mosaic generation without requiring excessive overlap. This partial overlap approach maintains monitoring completeness while reducing redundant data capture and improving flight efficiency.
Solution Approach 2:
The system performs preliminary geo-referencing and orthorectification during the image capture phase by recording precise GPS and attitude data, enabling efficient post-processing and reducing the need for extensive overlap to ensure complete coverage.
3Area of stationary object
If a wide-field of view (WFOV) camera is used to increase landscape coverage area, then productivity is improved, but measurement precision of spectral imagery deteriorates
Solution Approach 1:
The system merges the complementary data from two cameras with different fields of view. The WFOV camera provides broad spatial coverage while the NFOV MS camera provides high spectral resolution, and their images are stitched together to create a final product that achieves both wide coverage and high measurement precision.
Solution Approach 2:
The system applies local quality by using the NFOV MS camera to capture high-resolution spectral data of specific regions of interest within the broader WFOV coverage area, ensuring that critical areas receive detailed spectral analysis while maintaining overall landscape context.
Data Source
AI summary
Image acquisition and analysis systems for efficiently generating high resolution geo-referenced spectral imagery of a region of interest. In some examples, aerial spectral imaging systems for remote sensing of a geographic region, such as a vegetative landscape are disclosed for monitoring the development and health of the vegetative landscape. In some examples photogrammetry processes are applied to a first set of image frames captured with a first image sensor having a first field of view to generate external orientation data and surface elevation data and the generated external orientation data is translated into external orientation data for other image sensors co-located on the same apparatus for generating geo-referenced images of images captured by the one or more other image sensors.