Sky detection in images for solar exposure prediction

Abstract

This project describes a technique for segmenting regions of sky in an image from the remainder of the image. This segmentation technique is part of a method for predicting the solar exposure at a location of interest from a set of photographs. Given the latitude and longitude of the position and the direction and field of view of the camera it is possible to calculate the position of the sun in the image at a particular time on a particular day. If that position is in a sky region of the image then the location will be exposed to the sun at that time.

Critical to the success of this method for determining solar exposure is the image processing used to separate the sky from the rest of the image. This work is concerned with finding a technique which can do this for images taken under different weather conditions. The general approach to separate the sky from the rest of the image is to use the Canny edge detector and the morphology closing algorithm to find the regions in the image. The brightness and area of each region are then used to determine which regions are sky. The FloodFill algorithm is applied to identify all pixels in each sky region.

An extensive empirical study is used to find a set of threshold values for the Canny edge detector, applied to the blue colour channel, which allow successful identification of the sky regions in a wide range of images. Tests using different camera filters show that they do not usefully increase the contrast between the sky and the rest of the image, when a standard compact camera is used. The work reported in this thesis shows that this approach of finding edges to identify possible sky regions works successfully on a wide range of images although there will always be situations, such as when the image is taken directly into the sun, where manual adjustment to the identified regions may be required.