ABSTRACT
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.
ABSTRACT
In order to achieve housing automatic detection from high-resolution aerial imagery, the present paper utilized the color information and spectral characteristics of the roofing material, with the image segmentation theory, to study the housing automatic detection method. Firstly, This method proposed in this paper converts the RGB color space to HIS color space, uses the characteristics of each component of the HIS color space and the spectral characteristics of the roofing material for image segmentation to isolate red tiled roofs and gray cement roof areas, and gets the initial segmentation housing areas by using the marked watershed algorithm. Then, region growing is conducted in the hue component with the seed segment sample by calculating the average hue in the marked region. Finally through the elimination of small spots and rectangular fitting process to obtain a clear outline of the housing area. Compared with the traditional pixel-based region segmentation algorithm, the improved method proposed in this paper based on segment growing is in a one-dimensional color space to reduce the computation without human intervention, and can cater to the geometry information of the neighborhood pixels so that the speed and accuracy of the algorithm has been significantly improved. A case study was conducted to apply the method proposed in this paper to high resolution aerial images, and the experimental results demonstrate that this method has a high precision and rational robustness.
