博碩士論文 101522040 詳細資訊




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姓名 陳仕軒(Shih-Hsion Chen)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 在全天空影像中使用紋理特徵之雲分類
(Cloud Classification Using Texture Features in All-Sky Images)
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摘要(中) 因為環保議題愈來愈受重視的緣故,近年來有愈來愈多有關太陽能發電的研究。由於太陽能發電的效能主要仰賴地表所接收到的輻射量多寡,而天空中的雲朵是影響地表接收輻射最大的因素,因此若能對天空中的雲朵做即時性的觀測,分析目前主要分佈的雲層種類,便能藉此預測短期日射量的變化,以幫助太陽能電廠做電力上的調配與控管。但雲朵分類是項耗時且需長期觀測累積經驗方能確保正確性的工作,因此近來有人利用影像處理的技術對天空的雲朵進行分析,以幫助電廠自動化地瞭解未來某段時間內太陽受雲朵影響的程度,進而判斷是否需要採取其他相應措施。
然而,正如所有分類的問題會遭遇到的困難,雲朵的分類也存在著相當高的難度:除了相同種類的雲外型千變萬化,不易辨識,不同種類的雲有時也可能非常相似,而導致誤判。此外影像也容易因為受到太陽的高度曝光而使影像產生大面積且模糊的亮點,而使原本無雲的部分被電腦誤判為含有大片的雲朵,或是突然被遮蔽而使整個畫面突然偏暗,這些都是可能影響分類的重要因素。
為解決上述問題,本篇論文我們提出以區塊代替全影像進行分類的方法。首先,我們將全影像區分成數個等大的區塊,並從中取出統計特徵(Statistical Features)與區域紋理特徵(Local Texture Features),再利用最近鄰居(k-Nearest Neighbor)與支持向量機(Support Vector Machine)兩種方法分別對此兩類樣本個別訓練出分類模型。之後對影像中的區塊逐一分類,並且利用投票的方式決定該影像的最後分類結果。最後本實驗將顯示利用分割區塊法可以有效地降低多種雲同時出現的情形所造成的誤判,提高分類準確率。
摘要(英) With the increasing importance of environment protection, there are more and more research works in analyzing the clouds to help the solar plant to realize the effect caused by the clouds after a period of time. Because the ability to block the Sun differs from different kinds of clouds, automatically classifying the clouds will help the solar plant to allocate or manage the power system.
However, there are some difficulties existing in cloud classification. The large variety within the same cloud type and the similarity between the different cloud types both make the task more challenging. Besides, the dramatic light change caused by the relative position of the Sun and clouds will make it more difficult.
To deal with the problems, we make efforts in extracting more powerful features to classify different cloud types. Besides, we propose a method which use "block" instead of whole image to classify the mixed conditions. In our research, we divide the whole image into multiple equal-sized blocks, then extract statistical and local texture features from them. Then we’ll train models using k-Nearest Neighbors and Support Vector Machine. Before we start classify, we use Solar Position Algorithm to skip the blocks where the Sun locates in. After classification of each block, we use voting mechanism to decide the result of the image. In our experiment, we found that the proposed method outperforms the method using only whole image.
關鍵字(中) ★ 雲分類
★ 太陽位置偵測
關鍵字(英)
論文目次 摘要 i
Abstract vi
圖目錄 viii
表目錄 x
第一章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 3
1.3 系統流程與論文架構 6
第二章 資料與方法 9
2.1 遮罩濾除邊緣資訊 9
2.2 實驗資料介紹 10
2.3 區分影像區塊 13
2.4 太陽位置演算法 14
第三章 影像特徵與分類器 17
3.1 統計特徵 17
3.1.1 統計色彩特徵Statistical Spectral Features 17
3.1.2 統計紋理特徵 19
3.2 區域紋理特徵 22
3.2.1 區域二元特徵 22
3.2.3 區域三值特徵 26
3.3 主成分分析 28
3.4 分類器 30
3.4.1 最近鄰居演算法 30
3.4.2 支持向量機 31
第四章 實驗結果與分析 34
4.1 交叉驗證 35
4.2 格子點演算法 36
4.3 主成分分析降維並利用格子點取最佳參數 37
4.4 分類影像 39
第五章 結論與未來研究方向 45
參考文獻 46
參考文獻 [1] Heinle, Anna, Andreas Macke, and Anand Srivastav. "Automatic cloud
classification of whole sky images." Atmospheric Measurement Techniques Discussions 3.1 (2010): 269-299.
[2] Calbo, Josep, and Jeff Sabburg. "Feature extraction from whole-sky ground-based images for cloud-type recognition." Journal of Atmospheric and Oceanic Technology 25.1 (2008): 3-14.
[3] Cortes, Corinna, and Vladimir Vapnik. "Support-vector networks." Machine learning 20.3
(1995): 273-297.
[4] Haralick, Robert M., Karthikeyan Shanmugam, and Its′ Hak Dinstein. "Textural
features for image classification." Systems, Man and Cybernetics, IEEE Transactions on 6 (1973): 610-621.
[5] ^ T. Ojala, M. Pietikäinen, and D. Harwood (1994), "Performance
evaluation of texture measures with classification based on Kullback discrimination of distributions", Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, pp. 582 - 585.
[6] Ojala, Timo, Matti Pietikainen, and Topi Maenpaa. "Multiresolution gray-scale
and rotation invariant texture classification with local binary patterns." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.7 (2002): 971-987.
[7] Zhang, Baochang, et al. "Local derivative pattern versus local binary pattern:
face recognition with high-order local pattern descriptor." Image Processing, IEEE Transactions on 19.2 (2010): 533-544.
[8] Tan, Xiaoyang, and Bill Triggs. "Enhanced local texture feature sets for face
recognition under difficult lighting conditions." Image Processing, IEEE Transactions on 19.6 (2010): 1635-1650.
[9] Liu, Shuang, et al. "Illumination-invariant completed LTP descriptor for cloud
classification." Image and Signal Processing (CISP), 2012 5th International Congress on. IEEE, 2012.
[10] Principal Component Analysis: A Gentle Tutorial
[11] Principal Component Analysis: Concept, Geometrical Interpretation,
Mathematical Background, Algorithms, History, Practice
[12] Support Vector Machine - Regression (SVR)
http://www.saedsayad.com/support_vector_machine_reg.htm
[13] Reda, Ibrahim, and Afshin Andreas. "Solar position algorithm for solar radiation
applications." Solar energy 76.5 (2004): 577-589.
[14] Martínez-Chico, M., F. J. Batlles, and J. L. Bosch. "Cloud classification in a
mediterranean location using radiation data and sky images." Energy 36.7 (2011): 4055-4062.
[15] Slater, D. W., C. N. Long, and T. P. Tooman. "Total sky imager/whole sky
imager cloud fraction comparison." Eleventh ARM Science Team Meeting
Proceedings, Atlanta, Georgia. 2001.
[16] Campbell, David. "Widefield Imaging at Bayfordbury Observatory." (2010).
[17] Singh, G. K. "Solar power generation by PV (photovoltaic) technology: a
review." Energy 53 (2013): 1-13.
[18] Long, Charles N., et al. "Retrieving cloud characteristics from ground-based
daytime color all-sky images." Journal of Atmospheric and Oceanic Technology23.5 (2006): 633-652.
[19] Ineichen, Pierre, and Richard Perez. "A new airmass independent formulation for
the Linke turbidity coefficient." Solar Energy 73.3 (2002): 151-157.
[20] Huang, Hao, et al. "Correlation and local feature based cloud motion
estimation." Proceedings of the Twelfth International Workshop on Multimedia Data Mining. ACM, 2012.
[21] Marquez, Ricardo, and Carlos FM Coimbra. "Intra-hour DNI forecasting based
on cloud tracking image analysis." Solar Energy 91 (2013): 327-336.
[22] Shields, Janet E., et al. Research toward Multi-site Characterization of Sky
Obscuration by Clouds. SCRIPPS INSTITUTION OF OCEANOGRAPHY LA
JOLLA CA MARINE PHYSICAL LAB, 2009.
[23] Urquhart, Bryan, et al. "Towards intra-hour solar forecasting using two sky
imagers at a large solar power plant." Proceedings of the American Solar Energy
Society, Denver, CO, USA (2012).
[24] Wang, Fei, et al. "Short-term solar irradiance forecasting model based on
artificial neural network using statistical feature parameters." Energies 5.5
(2012): 1355-1370.
[25] Marquez, Ricardo, and Carlos FM Coimbra. "Forecasting of global and direct
solar irradiance using stochastic learning methods, ground experiments and the NWS database." Solar Energy 85.5 (2011): 746-756.
[26] Fu, Chia-Lin, and Hsu-Yung Cheng. "Predicting solar irradiance with all-sky
image features via regression." Solar Energy 97 (2013): 537-550.
[27] Stein, Joshua S., Clifford W. Hansen, and Matthew J. Reno. "The variability
index: A new and novel metric for quantifying irradiance and PV output variability." World Renewable Energy Forum, Denver, CO. 2012.
[28] Chow, Chi Wai, et al. "Intra-hour forecasting with a total sky imager at the UC
San Diego solar energy testbed." Solar Energy 85.11 (2011): 2881-2893.
[29] Cheng, Hsu-Yung, Chih-Chang Yu, and Sian-Jing Lin. "Bi-model short-term
solar irradiance prediction using support vector regressors." Energy 70 (2014): 121-127.
[30] Tom Stoffel, Dave Renné, Daryl Myers, Steve Wilcox, Manajit
Sengupta, Ray George, Craig Turchi , “Best Practices Handbook for
the Collection and Use of Solar Resource Data”, 2010.
[31] Zhang, Jie, et al. "Metrics for Evaluating the Accuracy of Solar Power
Forecasting." (2013).
[32] Ghonima, M. S., et al. "A method for cloud detection and opacity classification
based on ground based sky imagery." Atmospheric Measurement Techniques Discussions 5.4 (2012): 4535-4569.
[33] 工業技術研究院綠能與環境研究所工程及自動化研究室 “太陽能出力預測
技術”, 2012.
[34] Bryan Urquhart, “Sky Imager Solar Forecasting for Microgrid Optimization”,
2011.
[35] C. Cortes and V. Vapnik, “Support Vector Machine”, 1995.
[36] Basak, Debasish, Srimanta Pal, and Dipak Chandra Patranabis. "Support vector
regression." Neural Information Processing-Letters and Reviews 11.10 (2007): 203-224.
[37] Long, C. N., D. W. Slater, and Tim P. Tooman. Total sky imager model 880 status and
testing results. Pacific Northwest National Laboratory, 2001.
[38] Feng, Weijia, et al. "Calibration and rectification research for fish-eye lens
application." IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2011.
[39] 周瑞雄/陳梧桐/陳春明/孫惠民, “ Kalman Predictor and Multitarget Tracking
Algorithm”, 2003.
[40] Kleeman, Lindsay. "Understanding and applying Kalman
filtering." Proceedings of the Second Workshop on Perceptive Systems,
Curtin University of Technology, Perth Western Australia (25-26 January
1996). 1996.
[41] Tan, Xiaoyang, and Bill Triggs. "Enhanced local texture feature sets for face
recognition under difficult lighting conditions." Image Processing, IEEE Transactions on 19.6 (2010): 1635-1650.
[42] 曾定章, "第二章-影像處理基礎", 影像處理講義, p.35-39, 2013.
指導教授 鄭旭詠(HSU-YUNG CHENG) 審核日期 2014-8-22
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