基於SPOT-7衛星影像之台灣土地使用分析

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姓名

洪梓為(Zi-Wei Hung) 查詢紙本館藏

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資訊工程學系

論文名稱

基於SPOT-7衛星影像之台灣土地使用分析
(Land use analysis of Taiwan based on SPOT-7 satellite imagery)

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至系統瀏覽論文 (2026-8-1以後開放)

摘要(中)

研究土地利用有助於規劃和管理土地資源，傳統方式進行國土利用現況調查需要花費大量人力與時間成本來維護。本篇論文以區塊為單位，對台灣本島地區之 SPOT-7 衛星影像進行人工標註，收集台灣本島地區之建物道路、樹林、草地草原、農作物、水體、一般裸露地、農地裸露地七種類別的標註資料集。並應用地理物件影像分析 (GEOBIA) 的方法，將區塊化資料之影像直方圖(histogram image)作為資料輸入修改過之 ViT-B/16 模型，訓練一個基於自注意力機制 (self-attentioin) 的分類模型。本論文使用 2013 與 2021 兩年台灣本島地區的 SPOT-7 衛星影像，分別訓練兩個模型來預測各自的土地使用分類，並針對兩年的土地使用變遷進行研究與分析。

摘要(英)

Studying land use contributes to the planning and management of land resources. Traditional methods of conducting land use surveys require significant human and time resources for maintenance. In this paper, using the block as the unit, we manually labeled SPOT-7 satellite imagery of Taiwan, collecting labeled datasets for seven categories: buildings/roads, forests, grasslands, crops, water bodies, general bare land and agricultural bare land. By applying the method of Geographic Object-Based Image Analysis (GEOBIA), we used the histogram distribution of the block-level data as input to the modified ViT-B/16 model, which is based on self-attention mechanism, to train a classification model. Two models were trained using SPOT-7 satellite imagery of Taiwan for the years 2013 and 2021, respectively, to predict land use classifications for each year. The land use changes between the two years were studied and analyzed.

關鍵字(中)

★ 衛星影像
★ 深度學習
★ 自注意力機制
★ 分類模型
★ 土地利用

關鍵字(英)

★ Satellite imagery
★ Deep learning
★ Self-attention mechanism
★ Classification model
★ Land use

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章緒論 1
1-1 研究動機 1
1-2 相關文獻啟發 2
1-2-1 Transformer 3
1-2-2 Vision Transformer 4
第二章研究資料 6
2-1 SPOT-7 衛星影像 6
2-2 研究區域 9
第三章研究方法 11
3-1 資料集建立 12
3-1-1 超像素 12
3-1-2 SLIC 12
3-1-2 收集方法 13
3-2 地理物件影像分析 20
3-2-1 影像直方圖 20
3-2-2 NDVI 21
3-3 模型架構 23
第四章實驗與實驗結果討論 25
4-1 訓練資料、測試資料 25
4-2 實驗說明 25
4-3 實驗結果 27
第五章結論 37
5-1 結論 37
5-2 未來展望 37
參考文獻 38

參考文獻

[1] He, Jie, Du Lyu, Liang He, Yujie Zhang, Xiaoming Xu, Haijie Yi, Qilong Tian, Baoyuan Liu, and Xiaoping Zhang. “Combining Object-Oriented and Deep Learning Methods to Estimate Photosynthetic and Non-Photosynthetic Vegetation Cover in the Desert from Unmanned Aerial Vehicle Images with Consideration of Shadows.” Remote Sensing 15, no. 1 (January 2023): 105.
[2] Chen, Liang-Chieh, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” arXiv, August 22, 2018.
[3] Zhao, Hengshuang, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. “Pyramid Scene Parsing Network.” arXiv, April 27, 2017.
[4] Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” arXiv, May 18, 2015.
[5] Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. “Attention Is All You Need.” arXiv, December 5, 2017.
[6] Zaremba, Wojciech, Ilya Sutskever, and Oriol Vinyals. “Recurrent Neural Network Regularization.” arXiv, February 19, 2015.
[7] Bengio, Y., P. Simard, and P. Frasconi. “Learning Long-Term Dependencies with Gradient Descent Is Difficult.” IEEE Transactions on Neural Networks 5, no. 2 (March 1994): 157–66.
[8] Dosovitskiy, Alexey, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, et al. “An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale.” arXiv, June 3, 2021.
[9] Felzenszwalb, Pedro F., and Daniel P. Huttenlocher. “Efficient Graph-Based Image Segmentation.” International Journal of Computer Vision 59, no. 2 (September 2004): 167–81.
[10] Vedaldi, Andrea, and Stefano Soatto. “Quick Shift and Kernel Methods for Mode Seeking.” In Computer Vision – ECCV 2008, edited by David Forsyth, Philip Torr, and Andrew Zisserman, 705–18. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2008.
[11] Achanta, R., A. Shaji, K. Smith, A. Lucchi, P. Fua, and Sabine Süsstrunk. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods.” IEEE Transactions on Pattern Analysis and Machine Intelligence 34, no. 11 (November 2012): 2274–82.
[12] Hay, G. J., and G. Castilla. “Geographic Object-Based Image Analysis (GEOBIA): A New Name for a New Discipline.” In Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, edited by Thomas Blaschke, Stefan Lang, and Geoffrey J. Hay, 75–89. Lecture Notes in Geoinformation and Cartography. Berlin, Heidelberg: Springer, 2008.
[13] Hearst, M.A., S.T. Dumais, E. Osuna, J. Platt, and B. Scholkopf. “Support Vector Machines.” IEEE Intelligent Systems and Their Applications 13, no. 4 (July 1998): 18–28.
[14] Breiman, Leo. “Random Forests.” Machine Learning 45, no. 1 (October 1, 2001): 5–32.

指導教授

陳映濃(Ying-nong Chen)

審核日期

2023-7-27

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