A study of cloud classification with neural networks using spectral and textural features

The problem of cloud data classification from satellite imagery using neural networks is considered in this paper. Several image transformations such as singular value decomposition (SVD) and wavelet packet (WP) were used to extract the salient spectral and textural features attributed to satellite cloud data in both visible and infrared (IR) channels. In addition, the well-known gray-level cooccurrence matrix (GLCM) method and spectral features were examined for the sake of comparison. Two different neural-network paradigms namely probability neural network (PNN) and unsupervised Kohonen self-organized feature map (SOM) were examined and their performance were also benchmarked on the geostationary operational environmental satellite (GOES) 8 data. Additionally, a postprocessing scheme was developed which utilizes the contextual information in the satellite images to improve the final classification accuracy. Overall, the performance of the PNN when used in conjunction with these feature extraction and postprocessing schemes showed the potential of this neural-network-based cloud classification system.

[1]  J. Conover,et al.  Cloud interpretation from satellite altitudes , 1962 .

[2]  Mahmood R. Azimi-Sadjadi,et al.  Temporal updating scheme for probabilistic neural network with application to satellite cloud classification , 2000, IEEE Trans. Neural Networks Learn. Syst..

[3]  Azriel Rosenfeld,et al.  Automatic Segmentation and Classification of Infrared Meteorological Satellite Data , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

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

[5]  Melba M. Crawford,et al.  Cloud type discrimination via multispectral textural analysis , 1993, Defense, Security, and Sensing.

[6]  Roy L. Streit,et al.  Maximum likelihood training of probabilistic neural networks , 1994, IEEE Trans. Neural Networks.

[7]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[8]  Louis Garand,et al.  Automated Recognition of Oceanic Cloud Patterns. Part I: Methodology and Application to Cloud Climatology , 1988 .

[9]  Zhiqiang Gu,et al.  Textural And Spectral Features As An Aid To Cloud Classification , 1988, International Geoscience and Remote Sensing Symposium, 'Remote Sensing: Moving Toward the 21st Century'..

[10]  D. F. Specht,et al.  Generalization accuracy of probabilistic neural networks compared with backpropagation networks , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[11]  S. K. Sengupta,et al.  Cloud field classification based upon high spatial resolution textural features: 2. Simplified vector approaches , 1989 .

[12]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[13]  Olli Simula,et al.  Neural Network Based Cloud Classifier , 1995 .

[14]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[15]  P. Blonda,et al.  Comparison of backpropagation, cascade-correlation and Kokonen algorithms for cloud retrieval , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[16]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[17]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[18]  Sun-Yuan Kung,et al.  Face recognition/detection by probabilistic decision-based neural network , 1997, IEEE Trans. Neural Networks.

[19]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[20]  William J. Emery,et al.  An Automated Neural Network Cloud Classifier for Use over Land and Ocean Surfaces , 1997 .

[21]  M.R. Azimi-Sadjadi,et al.  Neural network-based cloud detection/classification using textural and spectral features , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[22]  Joann Parikh,et al.  A comparative study of cloud classification techniques , 1977 .

[23]  Marijke F. Augusteijn,et al.  Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier , 1995, IEEE Trans. Geosci. Remote. Sens..

[24]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[25]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[26]  Richard C. Dubes,et al.  Performance evaluation for four classes of textural features , 1992, Pattern Recognit..

[27]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[28]  C.F.N. Cowan,et al.  Textural and spectral features as an aid to cloud classification , 1991 .

[29]  Truong Q. Nguyen,et al.  Wavelets and filter banks , 1996 .

[30]  Li-jen Du,et al.  Texture Segmentation Of SAR Images Using Localized Spatial Filtering , 1990, 10th Annual International Symposium on Geoscience and Remote Sensing.

[31]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[33]  Ronald M. Welch,et al.  A neural network approach to cloud classification , 1990 .

[34]  M.R. Azimi-Sadjadi,et al.  Detection and classification of cloud data from geostationary satellite using artificial neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[35]  Pietro Burrascano,et al.  Learning vector quantization for the probabilistic neural network , 1991, IEEE Trans. Neural Networks.

[36]  Ronald M. Welch,et al.  Polar Cloud and Surface Classification Using AVHRR Imagery: An Intercomparison of Methods , 1992 .

[37]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[38]  Michel Desbois,et al.  Automatic classification of clouds on Meteosat imagery - Application to high-level clouds , 1982 .

[39]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Thomas H. Vonder Haar,et al.  A Bispectral Method for Cloud Parameter Determination , 1977 .

[41]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[42]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  D.A. Landgrebe,et al.  Classification with spatio-temporal interpixel class dependency contexts , 1992, IEEE Trans. Geosci. Remote. Sens..

[44]  W. Shenk,et al.  A Multispectral Cloud Type Identification Method Developed for Tropical Ocean Areas with Nimbus-3 MRIR Measurements , 1976 .

[45]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[46]  S. K. Sengupta,et al.  Structural and Textural Characteristics of Cirrus Clouds Observed Using High Spatial Resolution LANDSAT Imagery. , 1988 .