딥러닝 기반 농경지 속성분류를 위한 TIF 이미지와 ECW 이미지 간 정확도 비교 연구

Abstract

In this study, We conduct a comparative study of deep learning-based classification of agricultural field attributes using Tagged Image File (TIF) andEnhanced Compression Wavelet (ECW) images. The goal is to interpret and classify the attributes of agricultural fields by analyzing the differencesbetween these two image formats. “FarmMap,” initiated by the Ministry of Agriculture, Food and Rural Affairs in 2014, serves as the first digital mapof agricultural land in South Korea. It comprises attributes such as paddy, field, orchard, agricultural facility and ginseng cultivation areas. For thepurpose of comparing deep learning-based agricultural attribute classification, we consider the location and class information of objects, as well as theattribute information of FarmMap. We utilize the ResNet-50 instance segmentation model, which is suitable for this task, to conduct simulatedexperiments. The comparison of agricultural attribute classification between the two images is measured in terms of accuracy. The experimental resultsindicate that the accuracy of TIF images is 90.44%, while that of ECW images is 91.72%. The ECW image model demonstrates approximately 1.28%higher accuracy. However, statistical validation, specifically Wilcoxon rank-sum tests, did not reveal a significant difference in accuracy between thetwo images.