Reduced false alarm automatic detection of clouds ans shadows on SPOT images using simultaneous estimation

In this study, we propose an automatic approach for detecting clouds, cloud shadows and mist present on optical remote sensing images such as SPOT/HRVIR ones. This detection is necessary to not take their signal into account for land studies from remote sensing data, such as land cover / land use classification, vegetation and soil moisture monitoring. The adopted approach is based on Markov Random Field (MRF) modeling at two levels: pixel and object. The algorithm is parameterized by six parameters that are rather robust since their value was kept identical for the processing of 39 SPOT/HRVIR images that corresponds to various acquisition conditions, seasons, and landscapes. Our method makes use of three main cloud/shadow features: - Clouds (or shadows) can be viewed as connex objects; - Each cloud generates a shadow with similar shape and area; - The direction of the relative position of a cloud and its shadow in the image is determined by acquisition conditions. The first feature is modeled using a MRF on the pixel graph, and we show that the proposed model leads to the use of hysteresis threshold techniques or growing region as far as local optimization is concerned. The two last features are modeled using a MRF on the graph of cloud and shadow objects (detected from the previous step at pixel level), and we show that the proposed model corresponds the mutual validation of cloud and shadow detections.