博碩士論文 107623019 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:10 、訪客IP:3.235.227.117
姓名 胡翔(Xiang Hu)  查詢紙本館藏   畢業系所 太空科學與工程研究所
論文名稱 超解析成像法應用於光學衛星影像之比較
(Comparison of Super-Resolution Techniques for Optical Satellite Imagery)
相關論文
★ 利用高光譜影像作異常物偵測★ 利用電腦自動化對數值高程模型作線形偵測
★ 利用多光譜影像的光譜與空間資訊結合數學型態學進行海洋油汙偵測★ 利用遙測影像自動萃取校正點
★ 新的影像融合演算法應用於多光譜遙測影像★ 利用固定式攝影機即時偵測土石流
★ 藉由電腦視覺自動偵測土石流★ 利用多層模型於全波形光達分析樹冠結構
★ 利用MHE對多光譜影像輻射校正並 應用於土石流變遷偵測★ 單發多收合成孔徑雷達模擬與實驗
★ 福爾摩沙五號衛星影像壓縮之實現★ 電磁散射模型於粗糙表面之研究
★ 超寬頻Ka波段於樹之散射量測及研究分析★ 立體影像對自動特徵點提取進行三維重建
★ Comparison of Change Detection Methods Based on the Spatial Chaotic Model for Synthetic Aperture Radar Imagery★ 利用遙測影像偵測碟型天線之方向
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2022-7-31以後開放)
摘要(中) 遙測衛星的影像已經被廣泛的應用於生活中,像是農業的物種分析、水質的監測與環境變遷,都對生活帶來非常大的助益。但由於衛星的空間解析度受到限制,在光學衛星影像中,無法觀察出較為細部的地表特徵,因此許多超解析成像 (Super-Resolution imaging, SR) 方法已被發展,可以透過軟體及硬體的方式來提高空間解析度 (spatial resolution)。而傳統超解析成像是建立於插值法 (interoplation) 之上,雖然雙線性插值法、最近相鄰插值法或雙三次插值法能達到超解析的效果,但像素與像素間的的高頻細節卻不能被完整的修復。
本研究將三種影像特徵提取的超解析成像法,應用於光學衛星影像中並進行比較。稀疏編碼超解析成像法 (Sparse Coding Super-Resolution,SCSR),是基於單幅影像的超解析度重建。影像可以被表示為一個稀疏線性組合和經由影像訓練而來的字典 (dictionary),並且將低解析度中的影像塊(patches) 計算後所得到的稀疏表示係數應用於高解析度中進而重建高解析度影像;卷積神經網路超解析成像法 (Super-Resolution Convolutional Neural Network, SRCNN),則利用滑動視窗 (sliding window) 於不同隱藏層中進行影像的特徵提取,並且改變隱藏層中的權重以建立良好的神經網路來重建高解析度影像;快速卷積神經網路超解析成像法 (Fast Super-Resolution
Convolutional Neural Network, FSRCNN),進一步將卷積神經網路超解析成像法的神經網路進行修改,以達到更有效率的超解析成像及更高的精準度。
本實驗利用歐洲太空總署哥白尼計劃下的Sentinel-2 光學衛星影像,進
行超解析成像的測試,並且分析雙三次插值法、稀疏編碼超解析成像法、卷積神經網路超解析成像法及快速卷積神經網路超解析成像法的重建影像之表現,並且利用均方誤差 (Mean Square Error, MSE) 及峰值訊雜比 (Peak Signal-to-Noise Ratio, PSNR) 來比較不同方法、不同放大倍率和不同地表物覆蓋率的影像。在此研究中,快速卷積神經網路超解析成像法,有較高的峰值訊雜比,且相對於傳統的超解析成像法,有特徵提取超解析成像法在重建影像中有較為顯著的表現。
摘要(英) Satellite imagery has a wild range of applications, such as agriculture, water quality and environmental monitor. Because of the spatial resolution, some details
cannot be observed in the optical satellite images. Many super-resolution (SR) approaches have been developed to improve the spatial resolution with computer
software or hardware. But the traditional ways are based on interpolation, such as bilinear interpolation and bicubic interpolation, usually cannot restore the high frequency spatial information.
In this study, three methods based on feature extraction are applied to the same remote sensing images and their performances evaluation are also conducted.
The sparse coding super-resolution (SCSR) seeks a sparse representation for each patch of low-resolution (LR) input, and then use the coefficients of the representation to reconstruct the high-resolution (HR) image. On the other hand, super-resolution convolutional neural network (SRCNN) uses sliding windows in different hidden layers to extract the feature of LR, and then updates the previous weights to reconstruct the HR image. Finally, the fast super-resolution convolutional neural network (FSRCNN) improves the computational efficiency
of SRCNN by modifying the neural network structure.
The Sentinel-2 optical satellite images are adopted in this experiments. Four methods are implemented for performance evaluation, including the traditional
bicubic interpolation, SCSR, SRCNN and FSRCNN. The root-mean-square error (RMSE) and peak-signal-to-noise ratio (PSNR) are compared in three aspects, between different methods, scaling factors and land-covers. Preliminary results show the FSRCNN is the best, and all the feature extraction methods are outperform the traditional interpolation approach.
關鍵字(中) ★ 超解析
★ 深度學習
★ 神經網路
關鍵字(英) ★ super-resolution
★ deep learning
★ neural network
論文目次 摘 要 I
Abstract III
Contents IV
List of Figures VI
List of Tables VIII
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Overview 2
1.3 Thesis organization 3
Chapter 2 Literature Reviews 5
2.1 Super-resolution imaging 5
2.2 Recent methods of super-resolution imaging 7
2.3 Traditional super-resolution imaging 9
2.4 Deep learning for super-resolution imaging 10
Chapter 3 Methodology 12
3.1 Sparse coding super-resolution (SCSR) 12
3.2 Super-resolution convolutional neural network (SRCNN) 19
3.3 Fast super-resolution convolutional neural network (FSRCNN) 26
3.4 Image quality assessment (IQA) 29
3.5 Flow chart 30
Chapter 4 Research Meterials 32
4.1 Optical satellite 32
4.1.1 Sentinel-2 information 34
4.1.2 Sentinel-2 optical satellite imagery 35
4.2 Dataset 37
4.2.1 Study area 37
4.2.2 Image processing 38
Chapter 5 Experimential results and Dicussion 42
5.1 Experimental results 42
5.2 Discussion 49
5.2.1 Comparison of different methods 49
5.2.2 Comparison of different scaling factors 50
5.2.3 Comparison of different land-covers 53
Chapter 6 Conclusions and Future Works 55
6.1 Conclusions 55
6.2 Future works 56
Reference 57
參考文獻 [1] Greenspan, H. (2009). Super-resolution in medical imaging. The computer journal, 52(1), 43-63.
[2] Isaac, J. S., & Kulkarni, R. (2015, February). Super resolution techniques for medical image processing. In 2015 International Conference on Technologies for Sustainable Development (ICTSD) (pp. 1-6). IEEE.
[3] Lin, F., Fookes, C., Chandran, V., & Sridharan, S. (2007, August). Super-resolved faces for improved face recognition from surveillance video. In International Conference on Biometrics (pp. 1-10). Springer, Berlin, Heidelberg.
[4] Zhang, L., Zhang, H., Shen, H., & Li, P. (2010). A super-resolution reconstruction algorithm for surveillance images. Signal Processing, 90(3), 848-859.
[5] Dai, D., Wang, Y., Chen, Y., & Van Gool, L. (2016, March). Is image super-resolution helpful for other vision tasks?. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1-9). IEEE.
[6] Sajjadi, M. S., Scholkopf, B., & Hirsch, M. (2017). Enhancenet: Single image super-resolution through automated texture synthesis. In Proceedings of the IEEE International Conference on Computer Vision (pp. 4491-4500).
[7] Haris, M., Shakhnarovich, G., & Ukita, N. (2018). Task-driven super resolution: Object detection in low-resolution images. arXiv preprint arXiv:1803.11316.
[8] Keys, R. (1981). Cubic convolution interpolation for digital image processing. IEEE transactions on acoustics, speech, and signal processing, 29(6), 1153-1160.
[9] Timofte, R., De Smet, V., & Van Gool, L. (2014, November). A+: Adjusted anchored neighborhood regression for fast super-resolution. In Asian conference on computer vision (pp. 111-126). Springer, Cham.
[10] Yang, C. Y., & Yang, M. H. (2013). Fast direct super-resolution by simple functions. In Proceedings of the IEEE international conference on computer vision (pp. 561-568).
[11] Yang, J., Wright, J., Huang, T. S., & Ma, Y. (2010). Image super-resolution via sparse representation. IEEE transactions on image processing, 19(11), 2861-2873.
[12] Yang, J., Wang, Z., Lin, Z., Cohen, S., & Huang, T. (2012). Coupled dictionary training for image super-resolution. IEEE transactions on image processing, 21(8), 3467-3478.
[13] Dong, C., Loy, C. C., He, K., & Tang, X. (2015). Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2), 295-307.
[14] Dong, C., Loy, C. C., & Tang, X. (2016, October). Accelerating the super-resolution convolutional neural network. In European conference on computer vision (pp. 391-407). Springer, Cham.
[15] Johnson, J., Alahi, A., & Fei-Fei, L. (2016, October). Perceptual losses for real-time style transfer and super-resolution. In European conference on computer vision (pp. 694-711). Springer, Cham.
[16] Wang, Z., Chen, J., & Hoi, S. C. (2020). Deep learning for image super-resolution: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] Islam, M. M., Asari, V. K., Islam, M. N., & Karim, M. A. (2010). Super-resolution enhancement technique for low resolution video. IEEE Transactions on Consumer Electronics, 56(2), 919-924.
[18] Li, X., Hu, Y., Gao, X., Tao, D., & Ning, B. (2010). A multi-frame image super-resolution method. Signal Processing, 90(2), 405-414.
[19] Ning, B., & Gao, X. (2013). Multi-frame image super-resolution reconstruction using sparse co-occurrence prior and sub-pixel registration. Neurocomputing, 117, 128-137.
[20] Glasner, D., Bagon, S., & Irani, M. (2009, September). Super-resolution from a single image. In 2009 IEEE 12th international conference on computer vision (pp. 349-356). IEEE.
[21] Yang, J., Wright, J., Huang, T., & Ma, Y. (2008, June). Image super-resolution as sparse representation of raw image patches. In 2008 IEEE conference on computer vision and pattern recognition (pp. 1-8). IEEE.
[22] He, H., & Siu, W. C. (2011, June). Single image super-resolution using Gaussian process regression. In CVPR 2011 (pp. 449-456). IEEE.
[23] Sun, J., Xu, Z., & Shum, H. Y. (2008, June). Image super-resolution using gradient profile prior. In 2008 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE.
[24] Freeman, W. T., Jones, T. R., & Pasztor, E. C. (2002). Example-based super-resolution. IEEE Computer graphics and Applications, 22(2), 56-65.
[25] Zeyde, R., Elad, M., & Protter, M. (2010, June). On single image scale-up using sparse-representations. In International conference on curves and surfaces (pp. 711-730). Springer, Berlin, Heidelberg.
[26] Capel, D., & Zisserman, A. (2001, December). Super-resolution from multiple views using learnt image models. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 (Vol. 2, pp. II-II). IEEE.
[27] Duchon, C. E. (1979). Lanczos filtering in one and two dimensions. Journal of applied meteorology, 18(8), 1016-1022.
[28] Irani, M., & Peleg, S. (1991). Improving resolution by image registration. CVGIP: Graphical models and image processing, 53(3), 231-239.
[29] G. Freedman and R. Fattal, “Image and video upscaling from local self-examples,” TOG, vol. 30, 2011.
[30] Kim, K. I., & Kwon, Y. (2010). Single-image super-resolution using sparse regression and natural image prior. IEEE transactions on pattern analysis and machine intelligence, 32(6), 1127-1133.
[31] Xiong, Z., Sun, X., & Wu, F. (2010). Robust web image/video super-resolution. IEEE transactions on image processing, 19(8), 2017-2028.
[32] Yang, C. Y., Ma, C., & Yang, M. H. (2014, September). Single-image super-resolution: A benchmark. In European Conference on Computer Vision (pp. 372-386). Springer, Cham.
[33] LeCun, Y., Boser, B. E., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. E., & Jackel, L. D. (1990). Handwritten digit recognition with a back-propagation network. In Advances in neural information processing systems (pp. 396-404).
[34] Zeyde, R., Elad, M., & Protter, M. (2010, June). On single image scale-up using sparse-representations. In International conference on curves and surfaces (pp. 711-730). Springer, Berlin, Heidelberg.
[35] Chang, H., Yeung, D. Y., & Xiong, Y. (2004, June). Super-resolution through neighbor embedding. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. (Vol. 1, pp. I-I). IEEE.
[36] Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. science, 290(5500), 2323-2326.
[37] CGG, Passion for Geoscience. Retrieved from https://www.cgg.com/en/What-We-Do/GeoConsulting/NPA/Newsletters/March-2018-Issue-12/Introduction-to-Optical-Satellite-Imagery
[38] Satellite Imaging Corporation. Retrieved from https://www.satimagingcorp.com/satellite-sensors/other-satellite-sensors/spot-5/
[39] 李良輝、黃明哲、林奕翔、吳笛豪、張庭,2008,遙測影像資料庫建置關鍵技術及 基於內容的檢索研究(2/3),2-4。
指導教授 任玄(Hsuan Ren) 審核日期 2020-7-28
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明