模糊類神經網路為架構之遙測影像分類器設計

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呂維哲(Wei-Zhe Lu) 查詢紙本館藏

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論文名稱

模糊類神經網路為架構之遙測影像分類器設計
(A Neuro-fuzzy-based Approach to the Classification of Remotely Sensed Images)

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摘要(中)

遙測影像可以對地區進行規劃、自然資源的開發、環境監測、變遷偵測等提供很多資訊。這幾年來，大量的遙測影像取得並不困難，雖然分析者擅長辨識遙測影像的分類，但是常因為面對如此龐大的資料而不知所措。因此，大量的遙測影像並沒有真正地被有效應用，其原因在於沒有被有效的處理。基於這個理由，針對遙測影像發展一套自動化分類技術是必要的。在本論文中，我們首先使用多維度矩形複合式類神經網路來設計遙測影像分類器，透過足夠的訓練，可以直接由訓練樣本中萃取規則並以If-Then的方式表示出來，這些所萃取出的規則有助於分類的判斷使結果具有更高的可信度。此外，我們提出了一種新的分類器，稱為改良式簡化模糊適應共振理論映射圖（Modified Simplified Fuzzy Adaptive Resonance Theory Map，MSFAM），此網路是將模糊適應共振理論映射圖（Fuzzy Adaptive Resonance Theory Map，Fuzzy ARTMAP）作相當的簡化和改良而成。本論文最後以兩組遙測影像的資料集來驗證這兩種不同分類器的分類效果。

摘要(英)

Remotely sensed images offer much information on planning or exploitation of natural resources, monitoring environmentally sensitive areas, detecting sudden changes of areas, etc. Over the years, an extremely large volume of remotely sensed images is currently available. Although human interpreters often are superior in identifying land-cover/land-use on remotely sensed images, they may be overwhelmed by the amount of data. Therefore, a substantial part of these images is not optimally used because it has not been properly indexed. For this reason, it is necessary to develop a technique to automatically classify remotely sensed images. In this thesis, we first report the application of a class of HyperRectangular Composite Neural Networks (HRCNNs) for classification of remotely sensed multi-spectral image data. After sufficient training, the classification knowledge embedded in the numerical weights of trained HRCNNs can be successfully extracted and represented in the form of If-Then rules. These extracted rules are helpful to justify their responses so the classification results can be more trustable. In addition, we propose a new class of classifiers called Modified SFAM (MSFAM). MSFAM is a modified and simplified version of the well-known Fuzzy ARTMAP. Two sets of remotely sensed images are used to verify the performance of the two different classes of classifiers.

關鍵字(中)

★ 監督式分類法
★ 影像分類
★ 遙測影像
★ 類神經網路

關鍵字(英)

★ remotely sensed images
★ Neural Networks
★ supervised classification
★ image classification

論文目次

圖目錄 III
表目錄 V
第一章緒論 1
1.1研究動機 1
1.2論文架構 2
第二章遙測影像 3
2.1遙測影像的特性 3
2.2衛星的介紹 4
2.2.1法國史波特衛星 4
2.2.2美國大地衛星五號 6
2.3遙測影像的分類方式 6
2.3.1監督式分類法 7
2.3.2非監督式分類法 8
2.3.3監督式卅非監督式混合方法論 8
第三章分類器設計 10
3.1多維矩形複合式類神經網路 11
3.1.1網路架構 11
3.1.2 監督式決定導向學習演算法 12
3.1.3 模糊化 14
3.2模糊適應共振理論映射圖 17
3.2.1網路架構 17
3.2.2 模糊適應共振理論演算法 18
3.3簡化模糊適應共振理論映射圖 21
3.3.1網路架構 21
3.3.2監督式學習演算法 22
3.4模糊最小-最大分類器 24
3.4.1網路架構 24
3.4.2學習演算法 25
3.5 巢狀推廣範例系統 26
3.6.1模糊規則間的遞迴關係 28
3.6.2網路架構 30
3.7分類器設計之特性分析 31
第四章改良式簡化模糊適應共振理論映射圖 35
4.1網路架構 35
4.2學習演算法 36
4.3網路測試 42
4.4 範例說明 43
4.5網路特點 47
4.6模擬 49
4.6.1二維資料─579 49
4.6.2二維資料─雙螺旋 52
第五章實驗結果 58
5.1大地衛星多頻譜衛星影像 58
5.2空載多譜掃描儀DS-1260多頻譜遙測影像 65
第六章結論和展望 83
第七章參考文獻 86

參考文獻

[1] 工研院能資所遙測研究室, URL: http://rs.erl.itri.org.tw/.
[2] 中華民國航空測量及遙感探測學會, URL: http://www.csprs.org.tw/.
[3] 行政院國家太空計劃室籌備處, URL: http://www.nspo.gov.tw/.
[4] 國立中央大學太空及遙測研究中心, URL: http://www.csrsr.ncu.edu.tw/, http://www.csrsr.org.tw/.
[5] 曾忠一著，大氣衛星遙測學，渤海堂
[6] 林文賜, 周天穎, 林昭遠 “應用監督性類神經網路於衛星影像分類技術之探討,” 航測及遙測學刊第六卷第一期第41-58頁民國90年4月
[7] 黃凱易, “逐層分割群聚法及反覆移動均值群聚法於地覆非監督式分類之比較,” 航測及遙測學刊第五卷第三期第43-55頁民國89年9月
[8] S. Abe and M.-S. Lan, “A method for fuzzy rules extraction directly from numerical data and its application to pattern classification,” IEEE Trans. on Fuzzy Systems, vol. 3, no. 1, pp. 18-28, 1995.
[9] ─, “Fuzzy rules extraction directly from numerical data for function approximation,” IEEE Trans. Syst., Man, Cybern., vol. 25, no. 1, pp. 119-129, Jan. 1995.
[10] A. Baraldi and F. Parmiggiani, “A neural network for unsupervised categorization of multivalued input patterns: An application to satellite image clustering,” IEEE Trans. Geosci. Remote Sensing, vol. 33, no. 2, March 1995.
[11] A. M. Bensaid, L. O. Hall, J. C. Bezdek, L. P. Clarke, M. L. Silbiger, J. A. Arrington, and R. F. Murtagh, “Validity-guided (Re)Clustering with Applications to Image Segmentation ,” IEEE Trans. on Fuzzy Systems, vol 4, no. 2, pp. 112-123, May 1996.
[12] H. Bischof, W. Schneider, and A. J. Pinz, “Multispectral classification of landsat-images using neural networks,” IEEE Trans. Geosci. Remote Sensing, vol. 30, no. 3, pp.482-489, May 1992.
[13] M. Blume, D. A. Van-Blerkom, and S. C. Esener, “Fuzzy ARTMAP modification for intersecting class distribution,” World Congress on Neural Networks, Int. Neural Network Society 1996 Annual Meeting, Lawrence Erlbaum Assoc, USA, pp. 250-255, 1996.
[14] J. Bryant, “On the clustering of multidimensional pictorial data,” Pattern Recognit., vol. 67, pp. 115-125, 1979.
[15] G. A. Carpenter and N. Markuzon, “ARTMAP-IC and medical diagnosis: instance counting and inconsistent cases,” Neural Networks, vol. 11, no.2, pp.323-336, 1998.
[16] G. A. Carpenter and S. Grossberg, “A self-organizing neural network for supervised learning, recognition, and prediction,” IEEE Communications Msg., pp. 38-49, Sep. 1992.
[17] G. A. Carpenter, S. Grossberg, and D. B. Rosen, “A neural network realization of fuzzy ART,” Tech. Rep, CAS/CNS-TR-91-021, Boston, MA: Boston University, 1991.
[18] G. A. Carpenter, S. Grossberg, and D.B. Rosen, “ART2-A: An adaptive resonance algorithm for rapid category learning and recognition,” Neural Networks, vol. 4, pp. 493-504, 1991.
[19] G. A. Carpenter, S. Grossberg, and J. H. Reynolds, “ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network,” Neural Networks, vol. 4, pp. 565-588. 1991.
[20] G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, “Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps,” IEEE Trans. on Neural Networks, vol. 3, pp. 698-713, 1992.
[21] P. L. Chee and R. F. Harrison, “Modified fuzzy ARTMAP approaches Bayes’ optimal classification rates: an empirical demonstration,” Neural Networks, vol. 10, no. 4, pp. 755-774, 1997.
[22] S. W. Chen, C. F. Chen, M. S. Chen, C. Y. Fang, and K. E. Chang, “Neural-fuzzy classification for segmentation of remotely sensed images,” IEEE Trans. Signal Processing, vol. 45, no. 11, pp. 2639-2654, Nov. 1997.
[23] B. Chen and L. L. Hoberock, “ A fuzzy neural network architecture for fuzzy control and classification,” IEEE Int. Conf. on Neural Networks, vol. 2, pp. 1168-1173, New York, USA, 1996.
[24] G. B. Coleman and H. C. Andrews, “Image segmentation by clustering,” Proc. IEEE, vol. 67, pp. 773-785, 1979.
[25] I. Dagher, M. Geogiopoulos, G. L. Heileman, and G. Bebis, “Fuzzy ARTVar: an improved fuzzy ARTMAP algorithm,” IEEE Int. Joint Conf. on Neural Networks Proce.s. IEEE World Congress on Computational Intelligence, vol. 3, pp. 1688-1693, New York, USA, 1998.
[26] P. B. G. Dammert, J. I. H. Askne, and S. Kuhlmann, “Unsupervised segmentation of multitemporal interferometric SAR images,” IEEE Trans. Geosci. Remote Sensing, vol. 37, no. 5, Sep 1999.
[27] R. O. Duda and P. E. Hart, Pattern Classification and Science Analysis, New York: Wiley, 1973.
[28] K. S. Fu, “Pattern in remote sensing of the earth’s resources─invited paper,” IEEE Trans. Geosci. Electron., vol. GE-14, pp. 10-18, 1976.
[29] B. Gabrys, A. Bargiela, " General fuzzy min-max neural network for clustering and classification," IEEE Trans. on Neural Networks, vol. 11, pp. 769-783, 2000.
[30] P. Gong, D. J. Marceau, and P. J. Howarth, “A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data,” Remote Sens. Environ., vol. 40, pp. 137-151, May 1992.
[31] R. M. Haralick, “Statistical and structural approaches to texture,” in Proc. IEEE, vol. 67, pp. 786-804, May 1979.
[32] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Texture features for image classification,” IEEE Trans. Syst., Man, Cybern., vol. SMC-3, pp. 610-621, 1973.
[33] M. J. Healy and T. P. Caudell, “Guaranteed two-pass convergence for supervised and inferential learning,” IEEE Trans. on Neural Networks, vol. 9, no. 1, pp. 195-204, 1998.
[34] L. T. Hsien and J. L. Shie, “A neural network model for spoken word recognition,” IEEE Int. Conf. on System, Man, and Cybernetics, Computational Cybernetics and Simulation, , vol. 5, pp. 4029-4034, New York, USA, 1997.
[35] A. Jain and D. Zongker, “Feature selection: Evaluation, application, and small sample performance,” IEEE Trans. Pattern Anal. Machine Intell., vol. 19, pp. 153-158, Feb 1997.
[36] J. R. Jensen, Introductory Digit Image Processing-A Remote Sensing Perspective, Prentice-Hall, Inc., New Jersey. 1996.
[37] B. W. Jervis, T. Garcia, and E. P. Giahnakis, “Probabilistic Simplified Fuzzy ARTMAP (PSFAM),” IEE Proc.-Sci. Meas. Technol., vol. 146, no. 4, pp. 165-169,July 1999.
[38] T. Kasuba, “Simplified Fuzzy Adaptive Resonance Theory Map”, AI Expert, pp. 18-25, Nov 1993.
[39] J. M. Keller, M. R. Gray and J. A. Givens, “A fuzzy k-nearest neighbor algorithm,” IEEE Trans. Syst., Man, Cybern., vol. 15, no. 4, pp.258-263, 1985. Reprinted in Dasarthy, B. V., “Nearest neighbor pattern classification techniques,” IEEE Computer Society Press, Los Alamitos, CA, USA, pp. 276-284, 1991.
[40] K. S. Kuo, R. M. Welch, and S. K. Sengupta, “Structural and textural characteristic of cirrus clouds observed using high spatial resolution Landsat imagery,” J. Appl. Meteorol., vol. 27, pp. 1242-1260, Aug. 1988.
[41] K. J. Lang, and M. J. Witbrock, “Learning to tell two spirals apart,” in Proc. 1998 Connectionist Models Summer School, pp. 52-59, 1989.
[42] C. -T. Lin, Y. -C. Lee, and H. -C. Pu, “Satellite sensor image classification using cascaded architecture of neural fuzzy network,” IEEE Trans. Geosci. Remote Sensing, vol. 38, no. 2, pp.1033-1043, March 2000.
[43] A. Malkani and C. A. Vassiadis, “Parallel implementation of the fuzzy ARTMAP neural network paradigm on a hyercube,” Expert Systems, vol. 12, no. 1, pp. 39-53, 1995.
[44] S. Marriott and R. F. Harrison, “A modified fuzzy ARTMAP architecture for the approximation of noisy mappings,” Neural Networks, vol. 8, no. 4, pp. 619-641,1995.
[45] D. Michie, D. J. Spieglhater, and C. C. Taylor, “Machine learning, Neural and statistical classsifiction,” Ellis Horwood Series in Artifical Intellignce, England, 1994.
[46] B. Moore, “ART 1 and pattern clustering,” in Proc. 1988 Connectionist Models Summer School, pp. 174-185, 1989.
[47] P. M. Murphy and D.W. Aha. “Uci repository of machine learning databases,” 1991. Irvine, University of California, Department of Information and Computer Science, Anonymous, FTP: /pub/machine-learning-database on ics.uci.edu.
[48] D. R. Peddle and S. E. Franklin, “Image texture processing and data in tegration for surface pattern discrminiation,” Photogramm. Eng. Remote Sensing, vol. 57, no. 4, pp.413-420, 1991.
[49] J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction, Springer-Verlag Berlin Heidelberg, Third Edition, 1993.
[50] A. Rizzi, F. M. F. Mascioli, G. Martinelli, "Generalized min-max classifier," Proc. FUZZ-IEEE 2000, vol. 1, pp. 36-41, San Antonio, TX, May 2000.
[51] S. L. Salzberg, Learning with nested generalized exemplars, Ph.D. Thesis (Technical Report TR-14-89), Dept. of Computer Science, Harvard University, Cambridge, MA, 1989.
[52] ─, “Learning with nested generalized exemplars,” Hingham, MA: Kluwer Academic, 1990.
[53] ─, “A nearest hyperrectangle learning method,” Machine Learning, vol. 6, pp. 251-276, 1991.
[54] S. L. Salzberg foreword by W. A. Woods, Learning with nested generalized exemplars, Kluwer Academic, 1990.
[55] P. Simpson, “Fuzzy min-max neural networks,” in Proc. 1991 Int. Joint Conf. Neural Networks, pp. 1658-1669, Singapore, Nov. 18-21, 1991.
[56] ─, “Fuzzy min-max neural networks─Part 1: Classification,” IEEE Trans. on Neural Networks, vol. 3, no. 5, pp.776-786, Sept. 1992.
[57] ─, “Fuzzy min-max neural networks─Part 2: Clustering,” IEEE Trams. Fuzzy Syst., vol. 1, no. 32-45, Feb. 1993.
[58] N. Srinicasa, “Learning and generalization of noisy mapping using a modified PROBART neural network,” IEEE Trans. Sign Proc., vol. 45, no. 10, pp. 2533-2550, 1997.
[59] M. C. Su, "Use of neural networks as medical diagnosis expert systems," Computers in Biology and Medicine, vol. 24, no. 6, pp. 419-429, 1994.
[60] M. C. Su, C. T. Hsieh, and C. C. Chin, “A neuro-fuzzy approach to speech recognition without time alignment,” Fuzzy Sets and Systems, vol. 98, no. 1, pp. 33-41, 1998.
[61] M. C. Su, C. -W. Liu, and S. -S. Tsay, “Neural-network-based fuzzy model and its application to transient stability prediction in power systems,” IEEE Trans. Syst., Man, Cybern., vol. 29, no. 1, pp. 149-157, 1999.
[62] B.Tian, M. A. Shaikh, M. R. Azimi-Sadjadi, T. H. V. Haar, and D. L. Reinka, “A study of cloud classification with neural networks using spectral and textural features” IEEE Trans. on Neural Networks, vol.10, no. 1, pp. 138-150, January 1999.
[63] S. J. Verzi, G. L. Heileman, M. Georgiopoulos, and M. J. Healy, “Boosted ARTMAP,” IEEE Int. Joint Conf. on Neural Networks Proce.s, IEEE World Congress on Computational Intelligence, vol. 1, pp. 396-401, New York, USA, 1998.
[64] R. M. Welch, K. S. Kuo, S. K. Sengupta, and D. W. Chen, “Cloud field classification based upon high spatial resoulation textural feature (I): gray-level cooccurence matrix approach,” J. Geophys. Res., vol. 93, pp. 12663-12681, Oct. 1988.
[65] L. Zadeh, “Fuzzy sets,” Inform. Contr., vol. 8, pp. 338-353, 1965.

指導教授

蘇木春(Mu-Chun Su)

審核日期

2002-7-4

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