Thematic information detection for remote sensing image using SVM kernel functions

Thematic information detection is an important application of remote sensing image. Support vector machine (SVM) has been widely used in MODIS remote sensing detection. However, the difficulty of SVM application is how to select the suitable kernel function for remote sensing image. In this paper, the Sangeang Api volcanic ash cloud on May 30, 2014 is taken as an example, and the linear, polynomial, radial basis function (RBF) and sigmoid kernel functions are used to detect volcanic ash cloud from MODIS remote sensing image. And then the detected volcanic ash cloud information is evaluated in terms of simulation experiment and contrastive precision accuracy. The results show that the RBF kernel function is more effective and more robust for MODIS remote sensing image.

[1]  Alfred J Prata,et al.  Satellite detection of hazardous volcanic clouds and the risk to global air traffic , 2009 .

[2]  Gary P. Ellrod,et al.  Volcanic ash detection and cloud top height estimates from the GOES‐12 imager: Coping without a 12 μm infrared band , 2004 .

[3]  Lieven Clarisse,et al.  The infrared spectral signature of volcanic ash determined from high-spectral resolution satellite measurements , 2010 .

[4]  Cheng-Fan Li,et al.  Variational Bayesian independent component analysis-support vector machine for remote sensing classification , 2013, Comput. Electr. Eng..

[5]  Cheng-Fan Li,et al.  Volcanic ash cloud detection from remote sensing images using principal component analysis , 2014, Comput. Electr. Eng..

[6]  Jocelyn Chanussot,et al.  Support Vector Reduction in SVM Algorithm for Abrupt Change Detection in Remote Sensing , 2009, IEEE Geoscience and Remote Sensing Letters.

[7]  Chengfan Li,et al.  Using Improved ICA Method for Hyperspectral Data Classification , 2014 .

[8]  C. Kontoes,et al.  The potential of kernel classification techniques for land use mapping in urban areas using 5m-spatial resolution IRS-1C imagery , 2000 .

[9]  Aapo Hyvärinen,et al.  Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood , 1998, Neurocomputing.

[10]  Hui Wen-hua.  TM Image Classification Based on Support Vector Machine , 2006 .

[11]  Rama Chellappa,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Matching Shape Sequences in Video with Applications in Human Movement Analysis. Ieee Transactions on Pattern Analysis and Machine Intelligence 2 , 2022 .

[12]  José M. Bioucas-Dias,et al.  Does independent component analysis play a role in unmixing hyperspectral data? , 2005, IEEE Trans. Geosci. Remote. Sens..

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

[14]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[15]  Eufemia Tarantino,et al.  Accuracy assessment of per-field classification integrating very fine spatial resolution satellite sensors imagery with topographic data , 2001 .

[16]  Jonathan Dehn,et al.  Predicting Regions Susceptible to High Concentrations of Airborne Volcanic Ash in the North Pacific Region (Abstract Only) , 2004 .

[17]  Jenq-Neng Hwang,et al.  Object-based analysis and interpretation of human motion in sports video sequences by dynamic bayesian networks , 2003, Comput. Vis. Image Underst..

[18]  Alfred J Prata,et al.  Observations of volcanic ash clouds in the 10-12 μm window using AVHRR/2 data , 1989 .