Satellite Crop Identification With Multi-Scale Vegetation Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Long-term sequential satellite telemetry data for crop species identification is challenging due to high complexity, noise, and missing values, leading to excessive deviation in analytical outcomes.
Innovation Solution
A crop species identification system utilizing a receiver module, data cleaning module, feature extraction module with convolution kernels and pooling layers, and a classification module with a fully-connected layer to process and classify telemetric vegetation index data, including noise reduction and feature extraction techniques like linear interpolation and max/average pooling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If long-term sequential satellite telemetry data is used for crop species identification, then identification accuracy can be improved, but data complexity and noise increase causing excessive deviation in analytical outcomes
Solution Approach 1:
The patent applies preliminary action by implementing a data cleaning module that processes satellite telemetry data before it enters the classification system. This module handles missing values through interpolation, removes outliers using statistical methods, and smooths time series data to eliminate noise. By performing these cleaning actions in advance, the system ensures that only processed and validated data is used for species identification, thereby improving reliability while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary data cleaning module between the data collection phase and the classification phase. This intermediary component acts as a mediator that processes raw satellite telemetry data into cleaned, ready-for-classification data. The intermediary handles the complexity and noise through systematic processing steps, allowing the classification system to focus on accurate species identification without being directly exposed to raw data issues.
2Adaptability or versatility
If multiple convolution kernels are used to extract features at different time scales, then feature extraction capability is improved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the feature extraction process into multiple parallel convolutional layers, each with specialized kernels designed for specific time scales. Instead of using a single complex kernel, the system segments the temporal frequency spectrum and assigns different kernel types (e.g., low-pass for long-term trends, high-pass for short-term variations) to handle different aspects of the data independently. This segmentation allows the system to capture multi-scale features while distributing computational load across parallel processing units.
Solution Approach 2:
The patent applies dimensionality change by transforming the one-dimensional time series data into multi-dimensional feature space through convolutional operations. Each convolutional layer adds a new dimension of feature representation, allowing the system to simultaneously capture temporal patterns, seasonal variations, and long-term trends. This dimensional transformation enables rich feature extraction without requiring a single overly complex computational model.
3Reliability
If data cleaning and processing steps are added to handle noise and missing values, then reliability is improved, but processing time increases
Solution Approach 1:
The patent applies continuity of useful action by implementing efficient data cleaning operations that continuously process data without unnecessary interruptions. The cleaning module operates in a streamlined fashion, applying multiple processing steps (interpolation, outlier removal, smoothing) in a continuous pipeline rather than as separate batch operations. This continuous processing maintains data quality while minimizing idle time and processing delays.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting cleaning parameters based on data characteristics. The system monitors data quality metrics and adapts the intensity and type of cleaning operations accordingly. For example, if a time series shows high noise levels, the system increases smoothing operations; if missing values are sparse, it uses lighter interpolation methods. This adaptive parameter adjustment ensures reliable data cleaning while minimizing processing time by avoiding unnecessary aggressive cleaning when data quality is already sufficient.
Data Source
AI summary
A crop species identification system based on satellite telemetry data is disclosed, which includes: a receiver module, receiving plural telemetric vegetation indexes of a target area; a data cleaning module, cleaning anomalous data in the telemetric vegetation indices, to correspondingly generate cleaned index data; a feature extraction module, including at least two different convolution kernels for mapping the cleaned index data into at least two feature scale mapping data which respectively correspond to the convolution kernels, performing a pooling operation of the feature scale mapping data to generate a pooled data, and concatenating the at least two feature scale mapping data and the pooled data into concatenated data; and a classification module, including a fully-connected layer, for extracting features of the concatenated data, to generate a species classification result for the target area.