[논문]Sentinel-1 위성 영상을 활용한 침수 탐지 기법 방법론 비교 연구

이성우; 김완엽; 이슬찬; 정하규; 박종수; 최민하

doi:10.3741/jkwra.2024.57.3.181

Sentinel-1 위성 영상을 활용한 침수 탐지 기법 방법론 비교 연구
Comparative study of flood detection methodologies using Sentinel-1 satellite imagery

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.57 no.3, 2024년, pp.181 - 193

이성우 (성균관대학교 글로벌스마트시티융합전공) , 김완엽 (성균관대학교 글로벌스마트시티융합전공) , 이슬찬 (성균관대학교 수자원학과) , 정하규 (국립재난안전연구원 재난정보연구실) , 박종수 (국립재난안전연구원 재난정보연구실) , 최민하 (성균관대학교 수자원학과)

초록
AI-Helper

기후변화에 의해 발생하는 대기 불균형은 강우량의 증가로 이어지고, 침수 발생 빈도가 증가함에 따라 이를 탐지할 수 있는 기술의 필요성이 증가하고 있다. 침수 피해를 최소화하기 위해 지속적인 모니터링이 필요하며, 날씨의 영향을 받지 않는 합성개구레이더(Synthetic Aperture Radar, SAR) 영상을 활용하여 침수지역을 탐지하였다. 관측된 데이터는 median 필터를 통해 노이즈를 감소시키는 전처리 과정을 진행하였으며, 객체 탐지 기법을 통해 수체와 비수체를 분류하여 각 기법의 침수탐지 활용성을 평가하고자 하였다. 본 연구에서는 Otsu 기법과 SVM 기법을 통해 수체 및 침수 탐지를 수행하였으며, Confusion Matrix를 통해 전체적인 모델의 성능을 평가하였다. Otsu 기법은 수체와 비수체의 경계를 구분하는데 적합함을 보였으나, 혼합물의 영향을 받아 오탐지의 비율이 높게 나타났다. 반면, SVM 기법을 사용한 경우, 오탐지 비율이 낮고 혼합물에 의한 영향에 민감하지 않은 것으로 관측되었다. 이에 따라 침수 상태를 제외한 다른 조건에서 SVM 기법의 정확도가 높게 나타났다. Otsu 기법이 침수 조건에서 SVM 기법보다 다소 높은 정확도를 보였지만, 정확도의 차이가 5% 미만임을 확인할 수 있었다(Otsu: 0.93, SVM: 0.90). SVM 기법이 Otsu 기법보다 침수 전, 침수 후의 조건에서 정확도 차이가 최대 15% 이상 발생하여 수체 및 침수탐지에 더 적합하게 나타났다(Otsu: 0.77, SVM: 0.92). 이러한 결과는 SVM 기법이 수체 및 침수탐지에서 효과적으로 활용될 수 있음을 시사하며, 미래의 수재해 탐지 시스템에 적용될 때 유용한 정보를 제공할 수 있을 것으로 기대된다.

Abstract ▼ AI-Helper

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.

주제어

표/그림 (17)

그림 Fig. 1. Location of the hyderabad (Downstream of the Indus river, Pakistan)
그림 Fig. 2. Flowchart of methodology detecting the water body by using Otsu and SVM
표 Table 1. Confusion matrix
표 Table 2. Evaluation metric of AUC
그림 FIg. 3. Label data used as the ground truth for predicted data; (a) before flood, (b) during flood, and (c) after flood
그림 Fig. 4. Comparison between SVM (a-c) and Otsu's technique (d-f) (the regions are classified as into four categories: Water body, Undetected, Misclassification, and Non Water body)
그림 Fig. 5. Comparison between Label Data and NDWI Data for assessing the suitability of NDWI as verification data
표 Table 3. Confusion Matrix for Water body detection shown
그림 Fig. 6. Pre-flood results of SVM technique for detecting water body changes
표 Table 4. Area results according to flood state under the ROC curve (AUC)
그림 Fig. 7. Flood occurrence results of SVM technique for detecting water body changes
표 Table 5. Model accuracy according to flood state
표 Table 6. Pixel changes in the confusion matrix
그림 Fig. 8. Post-flood results of SVM technique for detecting water body changes
그림 Fig. 9. Identification of pre-flooding, flooding, and post-flooding conditions through the time series analysis of backscattering coefficient
그림 Fig. 10. Detection of flooded and non-flooded areas obtained by Label data, Otsu and SVM methods
표 Table 7. Pixel changes in the confusion matrix of Fig. 10

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