Feature Recognition in the Context of Automated Object-Oriented Analysis of Remote Sensing Data Monitoring the Iranian Nuclear Sites

摘要

Against the background of nuclear safeguards applications using commercially available satellite imagery, procedures for wide-area monitoring of the Iranian nuclear fuel cycle are investigated. Specifically, object-oriented classification combined with statistical change detection is applied to high-resolution imagery. In this context, a feature recognition and analysis tool, called SEaTH, has been developed for automatic selection of optimal object class features for subsequent classification. The application of SEaTH is presented in a case study of the NFRPC Esfahan, Iran. The transferability of classification models is discussed regarding the necessity for automation of extensive monitoring tasks.