Block-based cloud classification with statistical features and distribution of local texture features

Abstract

Abstract. This work performs cloud classification on all-sky images. To deal with mixed cloud types in one image, we propose performing block division and block-based classification. In addition to classical statistical texture features, the proposed method incorporates local binary pattern, which extracts local texture features in the feature vector. The combined feature can effectively preserve global information as well as more discriminating local texture features of different cloud types. The experimental results have shown that applying the combined feature results in higher classification accuracy compared to using classical statistical texture features. In our experiments, it is also validated that using block-based classification outperforms classification on the entire images. Moreover, we report the classification accuracy using different classifiers including the k-nearest neighbor classifier, Bayesian classifier, and support vector machine.

Cloud classification of ground-based infrared images combining manifold and texture features

“…To exhibit the recognition performance of the proposed method, we also compare with the other two models (Liu et al, 2015;Cheng and Yu, 2015) to assess its performance in this experiment. Liu's model employs WLBP feature with the KNN classifier based on the chi-square distance while Cheng's method adopts the statistical and uniform LBP features with the 10 Bayesian classifier.…”

“…And then the KNN classifier based on the chi-square distance was employed for cloud type recognition. Cheng and Yu (2015) incorporated statistical features and local texture features for block-based cloud classification. As Cheng and Yu (2015) reported, the method combining the statistical and 30 uniform LBP features with the Bayesian classifier (Bensmail and Celeux, 1996) displayed the best performance in the 10-fold cross validation overall.…”

From pixels to patches: a cloud classification method based on a bag of micro-structures

“…This method extracts features by projecting logarithmic magnitude of fast Fourier transform coefficients of a cloud image on the x axis in frequency domain. Cheng and Yu (2015) presented a block-based cloud classification method, which divides an image into multiple blocks and identifies the cloud type for each block based on both statistical features and distribution of local texture features.…”

“…Spectral features describe the average color and tonal variation of a cloud image (Heinle et al, 2010;Xia et al, 2015). Textural features refer to the spatial distribution of pixel intensity within a cloud image, i.e., homogeneity, randomness, and contrast of the gray level differences of pixels (Singh and Glennen, 2005;Cheng and Yu, 2015;Liu and Zhang, 2015). Essentially, all these features are built upon pixels, which are encoded by RGB vectors as shown in Fig.…”

Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

“…Cheng and C.-L. Lin: Cloud detection in all-sky images tively correlated under most conditions. In addition to providing cloud coverage information, accurate cloud detection result could further improve the cloud type classification accuracy (Cheng and Yu, 2015b). It has been established that employing cloud type information in the process of shortterm irradiance prediction could yield more accurate prediction results (Cheng and Yu, 2015a).…”