Automatic cloud detection on high resolution images

Abstract

Remote sensing optical images are often cloudy and then partially unusable. Thus, cloud detection can optimize the image acquisition loop and the end-user image selection. The current pre-processing of SPOT images includes an automatic cloud and snow detection algorithm based on neural networks and fuzzy logic, which globally provides correct cloud masks but with a perfectible confidence. This process must be improved in order to avoid systematic manual re-notation. For Ple/spl acute/iades high resolution (HR) satellite, CNES experiments an innovative cloud detection method based on correlation and stereoscopic effect (B/H ratio) between images. Thanks to the quasi-simultaneous acquisition of the five spectral bands (panchromatic, red, blue, green and near infrared), this new method can be systematically applied, in addition to a radiometric one. This paper focuses on the new detection algorithm for high resolution images, which includes six main steps: (1) generation of 20 m resolution preview images in both multispectral and panchromatic bands, (2) estimation of the misregistration between panchromatic and multispectral previews using a geometric model, a global DTM and an image matching algorithm, (3) computation of the residual parallax consisting in the difference between prediction model and image matching, (4) cloud detection through high parallax value thresholding (5) radiometric analysis for snow and low altitude clouds detection, (6) masks fusion and confidence status computation. This method, still under assessment over various SPOT5 and Quickbird images, seems to be very promising. The results, presented in the paper, show that the method is efficient even for Quickbird images, in spite of the very low B/H ratio value (0.002) close to Ple/spl acute/iades one.