Histogram Equalization for Geospatial Index Mapping
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Solution Overview
Problem
Current index-based geospatial analysis methods, such as those using the Normalized Difference Vegetation Index (NDVI), face challenges in effectively visualizing and comparing changes over time due to data concentration within a narrow range, making it difficult to distinguish between different index values and monitor progress in agricultural fields or other remote sensing applications.
Innovation Solution
The proposed solution involves performing acre-to-acre and day-to-day index-based analyses using histogram-based mapping techniques, where each index value is represented by a color scale or greyscale, allowing for the redistribution of histogram values across multiple days to provide a more accurate and visually distinguishable representation of changes, and applying these methods to various remote sensing indices beyond NDVI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional index-based mapping methods are used to represent geospatial data, then the data can be visualized using standard color scales, but the data concentration within a narrow range makes it difficult to distinguish between different index values and monitor changes over time
Solution Approach 1:
The patent applies parameter changes by transforming the narrow-range index values through histogram equalization, which redistributes the frequency distribution of index values across the full dynamic range. This transformation stretches the compressed data values into a wider range, improving visual distinction between different index values while preserving the temporal change information that would otherwise be lost due to data concentration.
2Measurement precision
If histogram-based mapping is applied to redistribute index values, then visual distinction between different index values is improved, but the complexity of the analysis framework increases
Solution Approach 1:
The patent implements preliminary action by pre-computing histograms for each time period and storing them for later retrieval. This allows the system to perform complex histogram equalization transformations without recalculating during the comparison phase, thereby reducing the operational complexity when analyzing changes over time while still achieving improved visual distinction.
Solution Approach 2:
The patent uses copying by creating visual representations (maps) that overlay index values from different time periods onto the same geographic framework. This allows multiple datasets to be compared simultaneously using the same color scale, simplifying the analysis framework by providing a consistent visual language across different time periods while maintaining precise distinction between index values.
Data Source
AI summary
Index-based geospatial analysis may include applying a first and second index-based analysis for a set of imagery. The set of imagery may include a location-specific index values used to form a histogram for a single index image (e.g., for a single surveyed field). This first analysis may be referred to as an “acre-to-acre” mapping, which may be useful for identifying differences in indices (e.g., NDVI vegetative health) of different parts of the field from a single day. A second “day-to-day” index-based analysis may be performed by calculating a histogram for each set of imagery from multiple days, combining the histograms, and generating a single equal-area index map. The index map can be applied to redistribute the histogram values within multiple days of data, which may provide a more useful map of variation in each individual image and changes between images.