Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images: AutoCloud+
This addresses the need for efficient cloud detection in Earth observation data, which is crucial for remote sensing and environmental monitoring, though it appears incremental as an extension of existing EO systems.
The authors tackled the problem of automatically detecting clouds and cloud shadows in multi-source, multi-spectral Earth observation imagery, regardless of calibration or platform, and developed AutoCloud+, a system that also supports various EO-based applications like image enhancement and land cover classification.
The proposed Earth observation (EO) based value adding system (EO VAS), hereafter identified as AutoCloud+, consists of an innovative EO image understanding system (EO IUS) design and implementation capable of automatic spatial context sensitive cloud/cloud shadow detection in multi source multi spectral (MS) EO imagery, whether or not radiometrically calibrated, acquired by multiple platforms, either spaceborne or airborne, including unmanned aerial vehicles (UAVs). It is worth mentioning that the same EO IUS architecture is suitable for a large variety of EO based value adding products and services, including: (i) low level image enhancement applications, such as automatic MS image topographic correction, co registration, mosaicking and compositing, (ii) high level MS image land cover (LC) and LC change (LCC) classification and (iii) content based image storage/retrieval in massive multi source EO image databases (big data mining).