利用深度學習神經網路進行衛星影像的崩塌地辨識

Detecting Landslides in Satellite Images Using Deep Learning Neural Networks

10.29417/JCSWC.202203_53(1).0003

陳映融(Ying-Jung Chen)；繆紹綱(Shaou-Gang Miaou)；徐于軒(Yu-Hsuan Hsu)；林映丞(Ying-Cheng Lin)

崩塌地 ； 衛星影像 ； NDVI ； 深度學習神經網路 ； Landslide ； Satellite Image ； NDVI ； Deep Learning Neural Network

中華水土保持學報

53卷1期（2022 / 03 / 01）

25 - 34

繁體中文

本研究提出可不受地形與地表高度限制，而能快速準確地進行疑似崩塌地分析的系統。首先取得同一位置但不同時期的兩張衛星影像並分析其NDVI值的變化，在變化較大之處，以影像處理法偵測出影像中的變異點，最後使用Faster R-CNN深度學習網路判定變異點是否為崩塌地。本系統以農委會水土保持局BigGIS巨量空間系統的崩塌地資料為基準驗證系統的效能，所得精確率高達92.2 %。本系統還可輸出崩塌地的外圍輪廓，方便後續分析與運用。

This study proposes a system for quickly and accurately analyzing suspected landslides without terrain and surface height restrictions. Two satellite images are obtained in the same location but at different times, and the changes in their NDVI (Normalized Difference Vegetation Index) values are analyzed. When large changes occur, image processing methods are employed to detect image territorial variations, and the Faster R-CNN (Region-based Convolutional Neural Network), a deep learning network, is used to determine whether the territorial variation is a landslide. The performance of this system was evaluated using landslide data from the Big Geospatial Information System and the Soil and Water Conservation Bureau, Council of Agriculture; the resulting precision was 92.2 %. The system also outputs the outer contour of the landslide area to facilitate subsequent analysis and application.

1. 吳振發, C.F.,詹士樑, S.L.(2003)。常態化差異植生指數應用於都市綠地品質管制之探討。台灣土地研究，6(2)，17-42。
   連結：
2. 林穎東, Y.T.,張國楨, K.C.,楊啟見, C.J.(2018)。利用物件式導向進行崩塌地種類判釋、復育追蹤—以高雄市寶來地區為例。中華水土保持學報，49(2)，98-109。
   連結：
3. 黃彥寧, Y.N.,葉昭憲, C.H.(2020)。山區道路崩塌潛勢模式之建構與評估－以阿里山公路為例。中華水土保持學報，51(3)，55-64。
   連結：
4. Canny, J.(1986).A Computational Approach to Edge Detection A Computational Approach to Edge Detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,8(6),679-698.
5. Girshick, R.(2015).Fast R-CNN.2015 IEEE International Conference on Computer Vision (ICCV)
6. He, K.,Gkioxari, G.,Dollár, P.,Girshick, R.(2020).Mask R-CNN.IEEE Transactions on Pattern Analysis and Machine Intelligence,42(2),386-397.
7. LeCun, Y.,Bengio, Y.,Hinton, G.(2015).Deep Learning.Nature,521(7553),436-444.
8. Lee, E.J.,Liao, W.Y.,Lin, G.W.,Chen, P.,Wu, D.,Lin, C.W.(2019).Towards Automated Real-Time Detection and Location of Large-Scale Landslides through Seismic Waveform Back Projection.Geofluids,2019,1-14.
9. Liu, W.,Anguelov, D.,Erhan, D.,Szegedy, C.,Reed, S.,Fu, C.Y.,Berg, A.C.(2016).SSD: Single Shot MultiBox Detector.Computer Vision and Pattern Recognition (cs.CV),9905,21-37.
10. Max, J.,Karen, S.,Andrew, Z.,Koray, K.(2015).,未出版
11. Miao, Z.,Fu, K.,Sun, H.,Sun, X.,Yan, M.(2018).Automatic Water-Body Segmentation from High-Resolution Satellite Images via Deep Networks.IEEE Geoscience and Remote Sensing Letters,15(4),602-606.
12. Nie, S.,Jiang, Z.,Zhang, H.,Cai, B.,Yao, Y.(2018).Inshore Ship Detection Based on Mask R-CNN.IEEE International Geoscience and Remote Sensing Symposium
13. Redmon, J.,Divvala, S.,Girshick, R.,Farhadi, A.(2016).You Only Look Once: Unified, Real-Time Object Detection.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
14. Ren, S.,He, K.,Girshick, R.,Sun, J.(2017).Faster RCNN: Towards Real-Time Object Detection with Region Proposal Networks.IEEE Transactions on Pattern Analysis and Machine Intelligence,39(6),1137-1149.
15. Rouse, J.W.,Haas, R.H.,Schell, J.A.,Deering, D.,Deering, W.(1974).Monitoring Vegetation Systems in The Great Plains with ERTS.ERTS Third Symposium,1(A),301-317.
16. Simonyan, K.,Zisserman, A.(2015).,未出版
17. 余軒誼, H.I.(2020)。Taiwan, ROC，國立臺北教育大學社會與區域發展學系碩士班=National Taipei University of Education。
18. 吳建忠, C.C.(2007)。Taiwan, ROC，國立成功大學地球科學系專班=National Cheng Kung University。
19. 李欣輯, H.C.,蘇文瑞, H.J.(2017)。國家災害防救科技中心研究報告國家災害防救科技中心研究報告,Taiwan, ROC:國家災害防救科技中心=National Science & Technology for Disaster Reduction。
20. 黃郁晴, Y.C.(2020)。Taiwan, ROC，國立中央大學土木工程學系碩士班=National Central University。
21. 謝漢欽, C.F.,潘巍中, S.L.(2017)。應用大地衛星 8 號多光譜指標偵測臺灣全島海岸防風林生長狀況。林業研究專訊，24(5)，23-29。