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Per-pixel classification of clouds from whole sky HDR images
Signal Processing: Image Communication ( IF 3.5 ) Pub Date : 2020-07-23 , DOI: 10.1016/j.image.2020.115950
Pinar Satilmis , Thomas Bashford-Rogers , Alan Chalmers , Kurt Debattista

Accurately identifying cloud types in images has multiple uses from meteorological science to computer graphics, especially as clouds are a major factor influencing atmospheric radiative transport. Understanding which cloud types are present in an image is typically performed on a coarse scale, where cloud types are identified per image, but do not permit a finer, per-pixel granularity of labelling cloud types. This paper presents a novel approach which solves this problem via a per-pixel classification method for identifying cloud types based on High Dynamic Range imagery of skies. The proposed method requires minimal labelling of the training data, and utilizes a hierarchical patch-based feature extraction technique which describes the statistical and structural features about regions of the image. This enables the extraction of representative feature vectors which are used for subsequent labelling. This approach is the first to produce a per-pixel classification of cloud types from a single image, with an accuracy of 84%. Additionally, when applied to whole sky cloud classification, our results produce a 98.3% accuracy, which is competitive with the state-of-the-art.



中文翻译:

全天空HDR图像中云的每个像素分类

准确地识别图像中的云类型具有从气象科学到计算机图形学的多种用途,尤其是因为云是影响大气辐射传输的主要因素。了解图像中存在哪些云类型通常是在粗略的范围内执行的,其中按图像识别云类型,但不允许对像素云类型进行更精细的按像素粒度划分。本文提出了一种新颖的方法,该方法通过基于像素的高动态范围图像的基于像素的分类方法识别云类型来解决此问题。所提出的方法需要对训练数据进行最少的标记,并利用基于分层补丁的特征提取技术,该技术描述了有关图像区域的统计和结构特征。这使得能够提取用于后续标记的代表性特征向量。该方法是第一个从单个图像生成云类型的按像素分类的方法,精度为84%。此外,将其应用于整个天云分类时,我们的结果可产生98.3%的准确度,与最新技术相比具有竞争力。

更新日期:2020-08-04
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