A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones

This study assesses the performance of the support vector machine image classification technique in the context of a tropical coastal zone exhibiting low to medium scale development. The overall and individual classification results of this approach were compared to the maximum likelihood classifier and the artificial neural network techniques. A 15-m spatial resolution ASTER image of Koh Tao in Thailand was used for the test, and support vector machine was found to offer only limited improvements in classification accuracy over the other methodologies. The support vector machine did, however, show promise in dealing with the difficult challenge of separating human infrastructure such as buildings from other land cover types such as coastal rock and sandy beach which have very similar spectral signatures. The medium resolution ASTER image also proved highly suited to classifying coastal landscapes with this mix of land cover types. Additional research is needed to assess the full potential of the support vector machine in a weighted or layered classification, and to explore potential applications of this classification tool in other tropical environments.

[1]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[2]  John R. Jensen,et al.  Fuzzy learning vector quantization for hyperspectral coastal vegetation classification , 2006 .

[3]  Patrick Christie,et al.  What Are We Learning from Tropical Coastal Management Experiences? , 2000 .

[4]  Roger L. King,et al.  Multitemporal land use and land cover classification of urbanized areas within sensitive coastal environments , 2003, IEEE Trans. Geosci. Remote. Sens..

[5]  Paul M. Mather,et al.  The use of backpropagating artificial neural networks in land cover classification , 2003 .

[6]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[7]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[8]  P. Kandus,et al.  Land cover classification system for the Lower Delta of the Parana River (Argentina) : Its relationship with landsat thematic mapper spectral classes , 1999 .

[9]  C. Berlanga-Robles,et al.  Land Use Mapping and Change Detection in the Coastal Zone of Northwest Mexico Using Remote Sensing Techniques , 2002 .

[10]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[11]  Zhou Shi,et al.  Quantifying Land Use Change in Zhejiang Coastal Region, China Using Multi-Temporal Landsat TM/ETM+ Images , 2007 .

[12]  Paul M. Mather,et al.  Support vector machines for classification in remote sensing , 2005 .

[13]  J. Anthony Gualtieri,et al.  Support vector machines for hyperspectral remote sensing classification , 1999, Other Conferences.

[14]  Barnali M. Dixon,et al.  Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? , 2008 .

[15]  R. Tateishi,et al.  Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt , 2007 .

[16]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[17]  Guobin Zhu,et al.  Classification using ASTER data and SVM algorithms;: The case study of Beer Sheva, Israel , 2002 .

[18]  Giles M. Foody,et al.  Mapping specific habitats from remotely sensed imagery: Support vector machine and support vector data description based classification of coastal saltmarsh habitats , 2007, Ecol. Informatics.

[19]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[20]  Guangdao Hu,et al.  Multi-Classifier Systems (MCSs) of Remote Sensing Imagery Classification Based on Texture Analysis , 2008, ISICA.

[21]  John R. Jensen,et al.  Opening the black box of neural networks for remote sensing image classification , 2004 .

[22]  Giles M. Foody,et al.  Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes , 2004 .

[23]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Paul M. Mather,et al.  Assessment of the effectiveness of support vector machines for hyperspectral data , 2004, Future Gener. Comput. Syst..

[25]  Jean-François Mas,et al.  Mapping land use/cover in a tropical coastal area using satellite sensor data, GIS and artificial neural networks , 2004 .

[26]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.