Pixel Map Difference Metric for Spatial Data Comparison
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Solution Overview
Problem
Conventional similarity metrics fail to account for spatial relationships in data sets, limiting their effectiveness in comparing agricultural field values and images, particularly when dealing with varying sizes and shapes, and are not scalable for accurate comparisons across different fields.
Innovation Solution
An agricultural intelligence computer system generates pixel maps from non-image data, transforming values into pixel values and locations, and computes a difference metric using a matrix of metric coefficients based on spatial distances, allowing for normalized comparisons that are scalable across different pixel map sizes and shapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional similarity metrics are used to compare data values, then individual value differences can be measured, but spatial relationships between locations are not accounted for
Solution Approach 1:
The patent transforms scalar data values into pixel maps with spatial coordinates, adding a spatial dimension to the data representation. This allows comparison metrics to operate in both value space and spatial space simultaneously, capturing both magnitude differences and spatial relationships between locations.
Solution Approach 2:
The patent introduces pixel maps as an intermediary representation between raw data values and comparison metrics. These pixel maps serve as a bridge that encodes both value information and spatial relationship information, enabling metrics to capture both aspects without direct computation on raw data.
2Loss of information
If image comparison techniques are used to account for spatial relationships, then spatial patterns can be recognized, but the techniques are not scalable to images of different sizes and shapes
Solution Approach 1:
The patent transforms image comparison into parameter-based metric computation by representing images as vectors of pixel values and computing metrics based on value differences and spatial distance weights. This parameter transformation allows the same metric framework to be applied to images of varying sizes and shapes by adjusting the vector dimensions and distance calculations accordingly.
Solution Approach 2:
The patent creates a universal comparison framework that works across different image dimensions by formulating metrics in terms of vector operations and normalized spatial distances. The same metric equations can be applied to any image size or shape, making the comparison technique universally applicable rather than limited to specific image dimensions.
3Loss of information
If pixel maps are generated from non-image data to enable image comparison techniques, then spatial relationships can be preserved, but the comparison metrics must be scalable to various pixel map sizes
Solution Approach 1:
The patent replaces complex image processing operations with straightforward vector arithmetic and matrix operations. By representing pixel maps as vectors and metrics as mathematical operations on these vectors, the system achieves scalability through efficient linear algebra computations rather than traditional image processing algorithms.
Data Source
AI summary
Systems and methods for scalable comparisons between two pixel maps are provided. In an embodiment, an agricultural intelligence computer system generates pixel maps from non-image data by transforming a plurality of values and location values into pixel values and pixel locations. The non-image data may include data relating to a particular agricultural field, such as nutrient content in the soil, pH values, soil moisture, elevation, temperature, and/or measured crop yields. The agricultural intelligence computer system converts each pixel map into a vector of values. The agricultural intelligence computer system also generates a matrix of metric coefficients where each value in the matrix of metric coefficients is computed using a spatial distance between to pixel locations in one of the pixel maps. Using the vectors of values and the matrix of metric coefficients, the agricultural intelligence computer system generates a difference metric identifying a difference between the two pixel maps. In an embodiment, the difference metric is normalized so that the difference metric is scalable to pixel maps of different sizes. The difference metric may then be used to select particular images that best match a measured yield, identify relationships between field values and measured crop yields, identify and/or select management zones, investigate management practices, and/or strengthen agronomic models of predicted yield.