Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images: AutoCloud+

TL;DR

AutoCloud+ is an innovative system for automatic, spatial context-sensitive cloud and cloud-shadow detection in multi-source, multi-spectral Earth observation images, applicable across various platforms and supporting multiple EO applications.

Contribution

It introduces a versatile EO image understanding system capable of automatic cloud/cloud-shadow detection across diverse multi-source, multi-spectral EO imagery, regardless of calibration status.

Findings

Effective cloud/cloud-shadow detection in multi-source EO images.

Applicable to various platforms including UAVs.

Supports multiple EO applications like image correction and land cover classification.

Abstract

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).