Cloud is floating in the earth sky widely, irregularly and frequently. So it appears in the satellite imagery. The cloud in
the remote sensing imagery especially high resolution remote sensing imagery and aerial image will largely reduce the
remote sensing image quality and use ratio, hinder the further application and the subsequent processing. Cloud detection
accurately is a necessary and important step in the remote sensing image data analysis processing. So, a new cloud
detection method based on HSI color space and stationary wavelet transformation (SWT) according to the spectral
properties of cloud and the different with other objects is proposed in this paper. First, transform the RGB to HSI of
image; then SWT is implemented to achieve the low frequency; the last result of cloud detection is obtained by the
segmentation and edge extraction use SOBLE. The experiments show that the approach can detect the cloud accurately,
availably and quickly.
Shadow is one of the basic characteristics in urban remote sensed imagery. It affects the extraction of object’s edge, identification of objects and registration of images, so shadow detection has a great importance in urban remote sensing. In this paper, a kind of method with HSV is proposed to detect shadow from the color high resolution remote sensing imagery mainly through a series of processing steps including twice HSV transformation, self-adaptive segmentation, morphological closing operation and little area removing. At last, the ratio of the shadow is achieved according to the shadow area statistical analysis. The experiments show that the approach can detect the shadow accurately and availably.