Many outdoor images contain sky. The sky detection and segmentation is important for image enhancement, horizon detection, and obstacle avoidance in unmanned air vehicles. Most researches in sky detection and segmentation are for blue sky only. Our work is proposed to detect and segment three categories of skies: blue sky, cloudy sky, and sunset sky. There are two types of sky detection methods: pixel based detection and block based detection. The main advantage of pixel based detection is the high correct detection ratio. There are many descriptors used in object detection such as color descriptors, color-shape descriptors, and shape descriptors. This work studies sky detection and segmentation with defferent descriptor types. The sky is classified into blue sky, cloudy sky, and sunset sky. For each sky type, the sky is detected using pixel based detection and block based detection. We improve the sky detection ratio using K-HSV descriptors. The sky detection with K-HSV descriptors has 86.16% correct ratio for blue sky. We decrease The number of keypoints used in sky segmentation to 200 random selected keypoints of all dense sampling keypoints. The sky segmentation based on 200 color moment invariant descriptors obtained 78.25% for blue sky, 61.63% for cloudy sky, and 62.27% for sunset sky.

The sky is an essential component in outdoor images. Sky and cloud type classification has applications in many areas such as image enhancement and sky image retrieval. In this paper, we improve the sky and cloud type classification rate over existing methods. Our work is based on two classification stages: sky image classification stage and sky cloud type classification stage. In sky classification stage, the image is classified into blue sky, cloudy sky, and sunset sky. Due to the impact of descriptor selection in the sky classification, we investigate ten descriptors; we show that the classifiers based on color descriptors are more accurate than the classifiers based on shape descriptors in sky type classification. We improve the sky image classification ratio using K-HSV descriptors. The sky classification with K-HSV descriptors has 77.3% correct classification rate. In cloud type's classification stage, the cloud is classified based on the sky type. For both the blue sky and the sunset sky, the cloud type is classified into six types: cloudless, thin-cirrus, cirrus, cirrocumulus, cumulus, and cumulonimbus. In cloudy sky, the cloud type is classified into three types: stratus, stratocumulus, and altostratus. The clouds are classified based on their shape and color using Gist minimum distance classification. The average correct classification rate of the clouds classifier is over 85% for cloudless, cumulus clouds, and stratus clouds and over 60% for thin-cirrus, cumulonimbus, stratocumulus, and altostratus clouds.