Feature Based Image Classification by using Principal Component Analysis

Classification of different types of cloud images is the primary issue used to forecast precipitation and other weather constituents. A PCA based classification system has been presented in this paper to classify the different types of single-layered and multi-layered clouds. Principal Component Analysis (PCA) provides enhanced accuracy in features based image identification and classification as compared to other techniques. PCA is a feature based classification technique that is characteristically used for image recognition. PCA is based on principal features of an image and these features discreetly represent an image. The used approach in this research uses the principal features of an image to identify different cloud image types with better accuracy. A classifier system has also been designed to exhibit this enhancement. The designed system reads features of gray-level images to create an image space. This image space is used for classification of images. In testing phase, a new cloud image is classified by comparing it with the specified image space using the PCA algorithm.

[1]  Imran Sarwar Bajwa,et al.  PCA BASED CLASSIFICATION OF SINGLE-LAYERED CLOUD TYPES , 2005 .

[2]  Mahmood R. Azimi-Sadjadi,et al.  A multichannel temporally adaptive system for continuous cloud classification from satellite imagery , 2003, IEEE Trans. Geosci. Remote. Sens..

[3]  R. Welch,et al.  Automated Cloud Classification of Global AVHRR Data Using a Fuzzy Logic Approach , 1997 .

[4]  Mahmood R. Azimi-Sadjadi,et al.  Neural network-based cloud classification on satellite imagery using textural features , 1997, Proceedings of International Conference on Image Processing.

[5]  Cristina Conde,et al.  PCA vs low resolution images in face verification , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[6]  Richard L. Bankert,et al.  Cloud Classification of AVHRR Imagery in Maritime Regions Using a Probabilistic Neural Network , 1994 .

[7]  Osama Masoud,et al.  Online motion classification using support vector machines , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[8]  Nello Cristianini,et al.  Advances in Kernel Methods - Support Vector Learning , 1999 .

[9]  Alfred J Prata,et al.  Cloud-top height determination using ATSR data , 1997 .

[10]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[11]  Jordi Vitrià,et al.  Analyzing non-negative matrix factorization for image classification , 2002, Object recognition supported by user interaction for service robots.

[12]  Rongchun Zhao,et al.  Face Recognition Using Multi-feature and Radial Basis Function Network , 2003, VIP.

[13]  I.S. Bajwa,et al.  PCA based image classification of single-layered cloud types , 2005, Proceedings of the IEEE Symposium on Emerging Technologies, 2005..

[14]  B. Schölkopf,et al.  Advances in kernel methods: support vector learning , 1999 .

[15]  K. Liou,et al.  Remote sensing of cirrus cloud parameters using advanced very-high-resolution radiometer 3.7- and 1 O.9-microm channels. , 1993, Applied optics.

[16]  C. Heipke,et al.  Detecting road junctions by artificial neural networks , 2003, 2003 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas.

[17]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[18]  Chi-Fa Chen,et al.  Combination of PCA and Wavelet Transforms for Face Recognition on 2.5D Images , 2003 .