Detecting clouds in satellite imagery is becoming more important with increasing data availability, however many earth observation sensors are not designed for this task. In Satellite pour l’Observation de la Terre 5 (SPOT5) High Resolution Geometrical (HRG) imagery, the reflectance properties of clouds are very similar to common features on the earth’s surface, in the four available bands (green, red, near-infrared and shortwave-infrared). The method presented here, called SPOTCASM (SPOT cloud and shadow masking), deals with this problem by using a series of novel image processing steps, and is the first cloud masking method to be developed specifically for SPOT5 HRG imagery. It firstly detects marker pixels using image specific threshold values, and secondly grows segments from these markers using the watershed-from-markers transform. The threshold values are defined as lines in a 2-dimensional histogram of the image surface reflectance values, calculated from two bands. Sun and satellite angles, and the similarity between the area of cloud and shadow objects are used to test their validity. SPOTCASM was tested on an archive of 313 cloudy images from across New South Wales (NSW), Australia, with 95% of images having an overall accuracy greater than 85%. Commission errors due to false clouds (such as highly reflective ground), and false shadows (such as a dark water body) can be high, as can omission errors due to thin cloud that is very similar to the underlying ground surface. These errors can be quickly reduced through manual editing, which is the current method being employed in the operational environment in which SPOTCASM is implemented. The method is being used to mask clouds and shadows from an expanding archive of imagery across NSW, facilitating environmental change detection. Full article

A new water index for SPOT5 High Resolution Geometrical (HRG) imagery normalized to surface reflectance, called the linear discriminant analysis water index (LDAWI), was created using training data from New South Wales (NSW), Australia and the multivariate statistical method of linear discriminant analysis classification. The index uses all four image bands, and is better at separating water and non-water pixels than the two commonly used variations of the normalized difference water index (NDWI), which each only use two image bands. Compared across 2,400 validation pixels, from six images spanning four years, the LDAWI attained an overall accuracy of 98%, a producer’s accuracy for water of 100%, and a user’s accuracy for water of 97%. These accuracy measures increase to 99%, 100% and 98% if cloud shadow and topographic shadow masks are applied to the imagery. The NDWI achieved consistently lower accuracies, with the NDWI calculated from the green and shortwave infrared (IR) bands performing slightly better (91% overall accuracy) than the NDWI calculated from the green and near IR bands (89% overall accuracy). The LDAWI is now being routinely used on an archive of over 2,000 images from across NSW, as part of an operational environmental monitoring program. Full article