Detecting clouds using Neural Networks and generating cloud free Mosaics
T. Hosomura, P.K.M.M Pallewatta
Computer Science Division
Asian Institute of Technology
Bangkok, Thailand
Abstract
Certain areas of the earth's surface are constantly covered by clouds during most of the time of the year. Obtaining cloud free images of such areas is an extremely difficult task. Neural networks can be meaningfully used as classifiers in situations where the data to be classified is of non parametric nature. This will be the situation encountered when we consider clouds as one class and all the clear sky areas as another class. Traditional methods for cloud detection such as automatic thresholding techniques result in poor accuracies over high albedo surfaces such as snow, due to the similarities of spectral signatures, and incorporating texture features can improve the classification accuracy in these situations. It is expected that these methods will result in cloud detection accuracies of over 90% over high albedo surfaces. This paper discusses the application of the above techniques for detecting cloud contamination of 50m resolution MOS-1 data. The methods of obtaining cloud free mosaic images are also discussed.
Introduction
Obtaining cloud free images of the Earth is of prime importance in Remote Sensing. But unfortunately some areas of the earth are constantly covered by clouds obtaining cloud fee images of such areas is a difficult task. In this study an effort is been made to develop image processing software to obtain cloud free mosaic images of such areas using daylight images obtained at different times. To generate mosaic cloud free images the following work should be accomplished.
- Detect clouds and shadows in several images of the same area taken at different times.
- Forming a cloud mask.
- Registration of the images
- Intensity matching of the segments of the mosaic.
- Constructing the mosaic.
Emphasis has been made on the fact that this system should be able to process all types of images, including of snow covered
areas, where cloud detection using only spectral bands is an extremely difficult task, due to the similarities of spectral signatures in all bands. In such situations, texture features should be used for cloud detection, and in areas where it is possible to detect clouds without texture features they should be dropped, since calculating texture features is computationally complex. Or alternatively texture features could be combined with spectral features for increased accuracy.
In a series of related works IEEE et a. (2) have used a back propagation neural network to classify cloud fields using texture measured derived from a single visible channel of Landsat MSS imagery. WELCH et al. (4) have has investigated the cloud and surface texture features in polar regions. In this study Grey Level Difference Vector (GLDV) and sum and Difference Histogram Approach (SDHA) texture features have been investigated. These texture features are simpler to compute than the traditional Grey Level Co-occurrence matrix texture features but result in similar accuracies in detecting cloud fields (4). A comprehensive analysis of GLCM, GLDV and SDHA texture features are given in (1), (2), (4).
