Automated Spectral Selection for Remote Sensing Feature Quantification

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Solution Overview

Problem

Current software lacks the ability to automatically transform spectral signatures in remotely sensed imagery into quantifiable information, requiring users to rely on visual and qualitative interpretations, which is inefficient and inaccurate for applications like precision agriculture.

Innovation Solution

An automated spectral selection system that includes a spectral selection processing module and a user device module, capable of detecting, identifying, and quantifying spatial patterns in imagery, using processors, non-transitory memory, and executable instructions to generate maps, target pixel values, and vectors, with optional machine learning and GPS integration for precise feature extraction and analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If automated spectral selection and feature extraction is implemented, then measurement precision and productivity are improved, but device complexity increases

Engineering Contradiction:

Improvefeature quantification accuracyVSAvoidsoftware system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs automated spectral selection and feature extraction without requiring expert intervention. The software independently identifies spectral patterns, selects relevant bands, extracts features, and quantifies them, making the complex analysis process self-executing and accessible to non-experts.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system automatically adjusts spectral parameters and processing settings based on the input imagery and analysis goals. It dynamically selects optimal spectral bands and processing parameters rather than requiring manual configuration, simplifying the interface while maintaining high measurement precision.

Inventive Principle:

Principle #35Parameter changes

2Loss of information

If comprehensive spectral analysis is performed, then information completeness is improved, but processing time increases

Engineering Contradiction:

Improvespectral information extractionVSAvoidprocessing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system extracts only the most relevant spectral information and features from the full spectral dataset. It identifies and isolates key spectral patterns and features necessary for the specific analysis goal, discarding redundant information to reduce processing time while maintaining information completeness.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The spectral analysis process is divided into discrete segments: spectral pattern identification, feature selection, feature extraction, and quantification. This segmentation allows the system to process different aspects independently and efficiently, reducing overall processing time while comprehensively analyzing spectral information.

Inventive Principle:

Principle #1Segmentation

3Productivity

If automated feature extraction is implemented, then productivity is improved, but ease of operation decreases

Engineering Contradiction:

Improveanalysis throughputVSAvoiduser interface simplicity

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The software provides a universal interface that handles multiple analysis tasks through a single consistent workflow. Users can perform spectral pattern identification, feature extraction, and quantification using the same simplified interface, making the system easy to operate while maintaining high productivity across different analysis types.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Ease of operation

If visual interpretation methods are used, then ease of operation is maintained, but measurement precision deteriorates

Engineering Contradiction:

Improvemethod simplicityVSAvoidfeature quantification accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system replaces manual visual interpretation with automated computational analysis. The software uses spectral pattern recognition and feature extraction algorithms to objectively identify and quantify features, eliminating the subjectivity and imprecision of visual methods while keeping the interface simple for users.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11948354B2Automated spectral selection for feature identification from remote sensed images

Publication Date: 2024.04.02 GEVO INC

AI summary

Disclosed are systems, methods, and apparatus related to automated spectral selection for feature identification from remote sensed images. The invention includes various modules, such as a spectral selection processing module and a user device module that are communicatively coupled to each other via a communication connection. The invention includes a non-transitory memory that causes a processor to carry out one or more tasks. Those tasks include, but are not limited to, storing imagery; generating a list of imagery, input related to the imagery, a map view, target pixel values, and geometry related to the imagery; transforming pixels to vectors; conducting analytics within the identified vector features; quantifying data within identified vector features; and displaying the results on the user device module.