云的存在严重影响遥感影像质量。 在航空影像的获取过程中, 实时的云检测能够及时提供准确的云遮挡比例以评价影像质量, 进而指导飞行方案以获取满足质量要求的影像。 采用光谱特征阈值的方法, 通过分析云光谱的特性, 选取能够有效检测云的亮度特征I和归一化差值特征P进行组合。 为实现自动检测, 在一维Otsu自动阈值和带限定条件Otsu阈值的基础上, 设计了阈值的分级配置策略为云特征配置合适的自动阈值, 策略的主要思想是: 首先采用多级分类标准对影像进行无云、 薄云、 厚云的类别判定, 再对不同类别的影像采取不同的特征阈值配置方案, 其中厚云影像的检测需要进一步分类配置阈值。 该策略实现了有云情况下能准确检测云、 无云情况下检测不到云的应用目标。 再结合选择性自动后处理方案, 真正做到云的自动、 高效、 准确的检测。 通过与不同方法的检测结果对比分析, 表明该方法的检测效率高, 精度满足实时质量评定的要求, 通用性强。

The present paper adopted a method based on the spectrum signatures with thresholds to detect cloud. Through analyzing the characteristic in the aspect of spectrum signatures of cloud, two effective signatures were explored, one was brightness signature I and the other was normalized difference signature P. Combined with corresponding thresholds, each spectrum condition can detect some cloud pixels. By composing the union of two spectrum conditions together, cloud can be detected more completely. In addition, the threshold was also very important to the accuracy of the detection result. In order to detect cloud efficiently, correctly and automatically, this paper proposed a new strategy about the assignment of thresholds to acquire suitable thresholds. Firstly, the images should be classified into three kinds of types which were images with no cloud, with thin cloud and with thick cloud. Secondly, different assignment methods of automatic thresholds of signatures would be adopted according to different types of images. For images with thick cloud, they would be further classified into three kinds by another standard and assigned by different thresholds integrated by automatic thresholds from other spectrum signatures. The automatic thresholds were acquired by Otsu algorithm and an improved Otsu algorithm. For images with thin cloud, the cloud would be detected by score algorithm. Due to this flexible strategy, cloud in images can be detected rightly and if there isn’t cloud in images the detection will be null to show that there is no cloud. Compared to the detection results of other different methods, the contrast results show that the efficiency of the detection method proposed in this paper is high and the accuracy satisfies the demand of real-time evaluation and the application range is wider.