Elsevier

Pattern Recognition Letters

Volume 49, 1 November 2014, Pages 193-200
Pattern Recognition Letters

Classification of clouds in satellite imagery using over-complete dictionary via sparse representation

https://doi.org/10.1016/j.patrec.2014.07.015Get rights and content

Abstract

Satellite imagery provides distribution information of cloud in a wide range of spatial and temporal scales. To improve the accuracy of weather forecasting and enhancing the effectiveness of climate monitoring, it is important to study the classification of clouds in satellite imagery. Motivated by the concept addressed in sparse representation using over-complete dictionary, a novel method for cloud data classification in satellite imagery was proposed. The intensity and spectral features attributed to different cloud types of samples were used to construct an adaptive over-complete dictionary in order to represent the cloud samples sparsely, followed by extracting the dictionary feature via sparse coding of samples. Then, the sparse representation coefficients matrix of training sample sets for specific cloud type was used to form a projection subspace. After orthonormal processing for the projection axis of the projection subspace, an effective sparse classifier was designed. Finally, the problem of cloud type identification for the test sample was solved according to the similarity between the test sample and specific cloud type subspace. Experiments were conducted on meteorological satellite data, the promising results indicated that the proposed approach can identify clear water (CW), clear land (CL), cumulonimbus (CB), altostratus & altocumulus (AS&AC), cirrostratus & cirrus-densus (CS&CD) and nimbostratus & cumulus (NS&CU) in the satellite images effectively, the classification accuracy is higher than that of the support vector machine classifier and traditional sparse representation classification classifier.

Introduction

Meteorological satellite imagery not only plays a crucial role in weather forecast, but also have promising application in monitoring some natural disasters, such as typhoon, flood, snowstorms and forest fire [7], [14]. Cloud type recognition using satellite imagery is helpful to obtain information about atmosphere, ocean and cloud conditions in all-weather; moreover, it also has great significance for weather monitoring and disaster prevention and mitigation [3], [6]. However, visual/manual interpretation is one of the major cloud recognition methods at present, the results are unreliable and operator dependent (influenced by the physiological status, psychological status, cognition, thinking and judging experience, etc.). This condition prevents the development of scientific, automation and quantitative trend for weather forecasting [12].

There are two key problems in automatic recognition of cloud type in satellite cloud image, feature extraction and classifier design, and the research of these two aspects are active. Based on the BP neural network, Perez et al. [14] proposed a way to identify stratocumulus and determine the macro and micro physical properties (cloud thickness, effective particle size and cloud temperature). Georgiev and Kozinarova [6] applied the multi-scale method to analyze the weather system and combined the potential vorticity theory of dynamic meteorology with satellite water vapor imagery to sketch the development trend of strong convective clouds. In 2005, based on the fractal theory, Junjie et al. [8] extracted the texture feature of cloud imagery and conduct the recognition experiment of cloud and clear sky with the neural networks. Liu et al. [11] through analyzing the texture pattern of the high-level cirrus, put forward a new method to forecast the laws of the typhoon intensity. However, under the circumstances of high-dimensional features and small sample, the artificial neural network used to recognize cloud type can hardly get a good result, which shows at least one of the following defects: poor generalization ability, slow learning speed, low convergence ability [2], [17].

To solve the problem of cloud recognition fundamentally, it is essential to study from the lowest level of image processing (that is, the image representation). For feature extraction, its essence is applying a few sparse coefficients to describe the major of image features [18]. Furthermore, due to the complexity and multiplicity of spectral properties coming from various clouds and their underlying surface, a single pixel is usually the comprehensive reflection of different clouds and surface features, we can believe that it is a linear combination of several components of cloud system in picture, which exactly coincide with the idea of sparse representation, that treats the image as a linear combination of multiple atoms [13], [5]. Regarding to the difficulty of cloud image classification, this paper introduces sparse representation in two aspects: feature extraction and classification algorithm. Initially, the grayscale and spectral characteristics of the samples from different cloud type were used to construct an adaptive over-complete dictionary in order to represent the cloud samples sparsely and regard the sparse coefficients as the dictionary feature of samples. Then, the sparse representation coefficients matrix of training sample, resulted from representing sparsely the various training sample using the adaptive dictionary in turn, was served as projection subspace of different cloud patterns. Finally, via orthonormal process for the projection axis of the projection subspace, we design an effective sparse classifier and achieve the cloud type recognition according to the similarity between the test sample and specific subspace. By processing real satellite data, this paper shows a scheme to recognize cloud type based on the sparse representation theory.

Section snippets

Cloud classification system for satellite imagery

Different cloud types have different microphysical properties and they run different dynamic processes, so the research on cloud classification is essential in understanding the rule of atmospheric evolution to improve the service of meteorological, and it can guide people to intervene atmospheric operation such as the artificial rainfall. At present, the meteorological satellite has become an important means of cloud detection. The significant task of the satellite image analysis is extracting

The sparse representation of cloud spectral feature and the extraction of dictionary feature

Due to each pixel in cloud image is generally a comprehensive reflection of different cloud type and underlying surface, if the original spectral features are directly used in cloud classifying, it is hard to achieve pleasing classification accuracy, so a new method of feature extraction is needed urgently. In this case, the goal of cloud classification should be indentifying every pixel of cloud image into appropriate cloud type which playing the dominant role of the pixel. In recent years,

Sparse classifier based on reconstruction residual

Lei et al. [10] reported a very interesting work by using sparse representation for pattern recognition based on reconstructed residual as follows.

Assuming that training samples yi  RM, i = 1, 2,  , N belong to n different types, respectively, and denoting mi as the number of the ith class training samples (number of total training samples is N=i=1nmi). These sample vectors are arranged in turn of class to construct a sample matrix Ψ = [D1, D2,  , Dn], Ψ  RM × N, and each column vector of the sub-matrix Di=[

Simulation results

This section presents the simulations conducted on real satellite data. The accuracy of CCSI-ODSR is evaluated; comparisons among CCSI-ODSR and some other traditional approaches are made. As mentioned in Section 2, six different classes representing background and clouds were defined. 10 daytime FY2D satellite data containing IR1, IR2, IR3, IR4, VIS channel imagery with all types of predefined classes are used in our simulations. Three meteorologists examine the selected cloud images and

Conclusions

This paper studies the sparse representation based solutions to cloud classification for satellite imagery and propose a new cloud classification scheme named as CCSI-ODSR in detail. The problem of the cloud classification in terms of similarity assessment between the test sample and specific cloud type subspace was formulated. Different cloud classifiers were benchmarked on their classification accuracy by data labeled by meteorologists, the results show that the proposed CCSI-ODSR is a better

Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under Grants 61271399; Science and Technology Project of Ningbo under Grants 2011A610192, 2013D10011; K.C. Wong Magna Fund in Ningbo University.

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This paper has been recommended for acceptance by Jie Zou.

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