Aerial Parcel Classification With Instance Segmentation Masks
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Solution Overview
Problem
Existing methods for recognizing the current state of agricultural product cultivation are time-consuming and expensive, making it difficult to predict prices and ensure market stability due to imbalances in supply and demand, particularly exacerbated by climate changes and natural disasters.
Innovation Solution
A method involving inputting aerial images to an instance segmentation model to generate parcel segmentation information, comparing digitized parcel data with instance segmentation information, and using a classification model trained with deep learning to assign classes to parcels based on image information and characteristic data, including temporal and spatial information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete enumeration of agricultural product cultivation is performed to recognize the exact current state, then classification accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent segments the aerial image into multiple patches and processes them in parallel using multiple classification models. Each patch is classified independently, then results are aggregated to achieve complete parcel classification. This segmentation enables efficient processing of large-scale agricultural areas while maintaining classification accuracy through distributed computation.
2Measurement precision
If complete enumeration of agricultural product cultivation is performed to recognize the exact current state, then classification accuracy is improved, but processing cost increases significantly
Solution Approach 1:
The patent creates multiple copies of the classification model to process different image patches simultaneously. Instead of using a single expensive comprehensive analysis, multiple lighter-weight model instances work in parallel on segmented portions of the image, reducing overall processing cost while achieving complete coverage through aggregation of individual patch results.
3Productivity
If instance segmentation model is used to generate parcel segmentation information, then classification efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent divides the complex task of classifying entire aerial images into smaller sub-tasks by segmenting images into patches. Each patch is processed by a dedicated classification model, reducing the computational burden on individual models. The segmentation approach transforms one complex classification problem into multiple simpler, parallelizable sub-problems.
4Measurement precision
If time-series aerial images are used for classification, then classification accuracy is improved, but data processing load increases
Solution Approach 1:
The patent segments time-series aerial images into multiple patches and processes them in parallel across multiple classification models. This segmentation approach enables efficient handling of large volumes of temporal data by distributing processing across multiple computational units, reducing the burden on individual processing channels while maintaining comprehensive temporal analysis for improved classification accuracy.
Data Source
AI summary
The present invention relates to a method, a system, and a computing-readable recording medium for classifying each parcel in an aerial image, in which the method includes: inputting the aerial image to an instance segmentation model to generate instance segmentation information for a parcel, and comparing digitized parcel data in a region included in the aerial image with the instance segmentation information to determine parcel object information in the aerial image; and inputting, based on the parcel object information of the aerial image, image information of each of a plurality of parcels extracted from the aerial image or image characteristic data including data derived from the image information to a classification model to assign a class to each of the parcels.