結合語義分割與全連接網路做基於坵塊的水稻判釋之初步研究

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姓名

施品妤(Pin-Yu Shih) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

結合語義分割與全連接網路做基於坵塊的水稻判釋之初步研究
(Integration of semantic segmentation and fully connected network for parcel-based rice segmentation: a preliminary study)

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摘要(中)

水稻是台灣重要的作物之一，每年政府都會需要了解其種植的區域與面積，並用於統計產量及訂定相關決策。傳統方法是經由專家對每張遙測影像進行判釋並人工繪製標記，然而這樣的方式效率很低，根本無法及時的提供大量調查的資訊。隨著近年人工智慧技術的發展，把相關技術應用於輔助判釋便可以大幅降低該項作業對人力的依賴，並能做到快速有效的水稻自動判釋。
目前應用於水稻判釋的方法是讓模型學習整張航照影像的資訊，然而對模型來說可能需要學習的資訊太多，因此本研究提出讓模型只在農地上作學習。另外，目前採用的語義分割模型是UNet，因其缺乏考慮像素間的空間關係，導致分割結果呈現破碎狀態，造成準確率降低，本研究將提出UNet-FNN架構來解決此問題。根據觀察，在不同地區的稻田有許多不同的樣貌，因此本研究將使用基於圖幅塊的隨機採樣來增加訓練資料的多樣性以訓練出更好的模型。

摘要(英)

Rice is one of the important crops in Taiwan. Every year, the government needs to know its planting area and the location, and use it to calculate the yield and make relevant decisions. The traditional method is that experts interpret each remote sensing image and draw labels manually. However, this method is very inefficient and cannot provide a large amount of survey information in time. With the development of artificial intelligence technology in recent years, the application of related technologies to assisted interpretation can greatly reduce the dependence on manpower, and achieve rapid and effective automatic interpretation of rice.
The current method used in rice segmentation is that model learns the information of the entire aerial image. However, there may be too much information for the model to learn. Therefore, this research proposes that the model only learns on farmland. In addition, the currently used semantic segmentation model is UNet. Because UNet does not consider the spatial relationship between pixels, it will lead to speckle segmentation results, resulting in reduced accuracy. This research will propose the UNet-FNN architecture to solve this problem. According to observations, there are many different appearances of rice fields in different area. Therefore, this study will use random sampling based on framelets to increase the diversity of training data to train a better model.

關鍵字(中)

★ 語義分割
★ UNet
★ 全連接網路
★ 水稻判釋

關鍵字(英)

★ Semantic segmentation
★ UNet
★ Fully connected network
★ Rice segmentation

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
一、緒論 1
1-1 研究背景 1
1-2 研究動機與目的 1
1-3 研究貢獻 2
1-4 論文架構 2
二、相關背景知識與研究 3
2-1 農業相關知識 3
2-1-1 水稻 3
2-1-2 坵塊 4
2-2 UNet-VGG16語義分割 4
2-2-1 UNet 4
2-2-2 VGG16 遷移式學習 5
2-3 Parcel-based Classification 6
2-4 語義分割應用於水稻判釋 7
三、研究方法 8
3-1 資料集介紹 8
3-2 資料前處理 10
3-3 水稻判釋模組 12
3-3-1 Pixel-based水稻判釋模組 12
3-3-2 Parcel-based 水稻判釋模組 14
3-4 評估方法 19
四、實驗與結果討論 22
4-1 實驗前準備 22
4-2 實驗一：Pixel-based水稻判釋模型 23
4-2-1 問題定義 23
4-2-2 實驗方法 23
4-2-3 結果與討論 24
4-3 實驗二：Parcel-based水稻判釋模型 25
4-3-1 問題定義 25

參考文獻

[1] 楊子緯：〈使用區域成長法改善語義分割造成的水稻坵塊破碎現象〉。碩士論文，國立中央大學，民國110年6月。
[2] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation.” Lecture Notes in Computer Science, pp. 234-241, 2015, doi: 10.1007/978-3-319-24574-4_28.
[3] Xin Zhao, Yitong Yuan, Mengdie Song, Yang Ding, Fenfang Lin, Dong Liang, Dongyan Zhang, “Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging.” Sensors, vol. 19, no. 18, p. 3859, 2019, doi: 10.3390/s19183859.
[4] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition.” International Conference on Learning Representations, 2015.
[5] Anindya Apriliyanti Pravitasari, Nur Iriawan, Mawanda Almuhayar, Taufik Azmi, Irhamah, Kartika Fithriasari, Santi Wulan Purnami, Widiana Ferriastuti, “UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation.” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 18, no. 3, pp. 1310-1318, 2020, doi: 10.12928/telkomnika.v18i3.14753.
[6] G. M. Smith and R. M. Fuller, “An integrated approach to land cover classification: An example in the Island of Jersey.” International Journal of Remote Sensing, vol. 22, no. 16, pp. 3123-3142, 2001, doi: 10.1080/01431160152558288.
[7] C. Yi, Y. Pan, and J. Zhang, “An Integrated Approach to Agricultural Crop Classification Using SPOT5 HRV Images.” Computer And Computing Technologies In Agriculture, Volume I, pp. 677-684, 2008, doi: 10.1007/978-0-387-77251-6_74.
[8] A. J. W. De Wit and J. G. P. W. Clevers, “Efficiency and accuracy of per-field classification for operational crop mapping.” International Journal of Remote Sensing, vol. 25, no. 20, pp. 4091-4112, 2004, doi: 10.1080/01431160310001619580.
[9] Thomas Blaschke, Stefan Lang, Eric Lorup, Josef Strobl and Peter Zeil, “Object-oriented Image Processing in an Integrated GIS/Remote Sensing Environment and Perspectives for Environmental Applications.” In: Cremers, A. B. & Greve, K. (Hrsg.), Umweltinformatik ’00 Umweltinformation für Planung, Politik und Öffentlichkeit. Marburg: Metropolis, 2000.
[10] S. Hojjatoleslami and J. Kittler, “Region growing: a new approach.”IEEE Transactions on Image Processing, vol. 7, no. 7, pp. 1079-1084, 1998, doi: 10.1109/83.701170.
[11] R. Adams and L. Bischof, "Seeded Region Growing", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 6, June 1994.
[12] J. Bromley, I. Guyon, Y. LeCun, E. Sackinger, and R. Shah. “Signature verification using a siamese time delay neural network.” J. Cowan and G. Tesauro (eds) Advances in Neural Information Processing Systems, 1993.
[13] J. Cohen, “A Coefficient of Agreement for Nominal Scales.”Educational and Psychological Measurement, vol. 20, no. 1, pp. 37-46, 1960, doi: 10.1177/001316446002000104.
[14] J. R. Landis, G. G. Koch, “The Measurement of Observer Agreement for Categorical Data.” Biometrics, vol. 33, no. 1, pp. 159-174, 1977, doi: 10.2307/2529310.

指導教授

梁德容(Deron Liang)

審核日期

2022-1-20

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