Weather Forecast Based on Color Cloud Image Recognition under the Combination of Local Image Descriptor and Histogram Selection

Abstract

Numerous researchers have used machine vision in recent years to identify and categorize clouds according to their volume, shape, thickness, height, and coverage. Due to the significant variations in illumination, climate, and distortion that frequently characterize cloud images as a type of naturally striated structure, the Local Binary Patterns (LBP) descriptor and its variants have been proposed as feature extraction methods for characterizing natural texture images. Rotation invariance, low processing complexity, and resistance to monotonous brightness variations are characteristics of LBP. The disadvantage of LBP is that it produces binary data that are extremely noise-sensitive and it struggles on regions of the image that are “flat” because it depends on intensity differences. This paper considers the Local Ternary Patterns (LTP) feature to overcome the drawbacks of the LBP feature. We also propose the fusion of color characteristics, LBP features, and LTP features for the classification of cloud/sky images. Morover, this study proposes to apply the Intra-Class Similarity (ICS) technique, a histogram selection approach, with the goal of minimizing the number of histograms for characterizing images. The proposed approach achieves better performance of recognition with less features in use by fusing LBP and LTP features and using the ICS technique to choose potential histograms.