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논문 기본 정보

자료유형
학술저널
저자정보
Ehsan Rahimi (안동대학교) 정철의 (안동대학교)
저널정보
대한원격탐사학회 대한원격탐사학회지 대한원격탐사학회지 제40권 제1호
발행연도
2024.2
수록면
57 - 69 (13page)
DOI
https://doi.org/10.7780/kjrs.2024.40.1.6

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초록· 키워드

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Crop classification plays a vital role in monitoring agricultural landscapes and enhancing foodproduction. In this study, we explore the effectiveness of Long Short-Term Memory (LSTM) models forcrop classification, focusing on distinguishing between apple and rice crops. The aim was to overcome thechallenges associated with finding phenology-based classification thresholds by utilizing LSTM to capturethe entire Normalized Difference Vegetation Index (NDVI) trend. Our methodology involves training theLSTM model using a reference site and applying it to three separate three test sites. Firstly, we generated25 NDVI images from the Sentinel-2A data. After segmenting study areas, we calculated the mean NDVIvalues for each segment. For the reference area, employed a training approach utilizing the NDVI trendline. This trend line served as the basis for training our crop classification model. Following the trainingphase, we applied the trained model to three separate test sites. The results demonstrated a high overallaccuracy of 0.92 and a kappa coefficient of 0.85 for the reference site. The overall accuracies for the testsites were also favorable, ranging from 0.88 to 0.92, indicating successful classification outcomes. We alsofound that certain phenological metrics can be less effective in crop classification therefore limitations ofrelying solely on phenological map thresholds and emphasizes the challenges in detecting phenology inreal-time, particularly in the early stages of crops. Our study demonstrates the potential of LSTM modelsin crop classification tasks, showcasing their ability to capture temporal dependencies and analyze time-series remote sensing data. While limitations exist in capturing specific phenological events, the integrationof alternative approaches holds promise for enhancing classification accuracy. By leveraging advancedtechniques and considering the specific challenges of agricultural landscapes, we can continue to refinecrop classification models and support agricultural management practices.

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