{'fraction_non_alphanumeric': 0.0273224043715847, 'fraction_numerical': 0.00234192037470726, 'mean_word_length': 5.442211055276382, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Simulation-Based Building Change Detection From Multiangle SAR Images and Digital Surface Models

This paper presents two change-detection strategies based on the fusion of scene knowledge and two high-resolution synthetic aperture radar (SAR) images (pre-event, postevent) with focus on individual buildings and facades. Avoiding the dependence of the signal incidence angle, the methods increase the flexibility with respect to near-real-time SAR image analysis after unexpected events. Knowledge of the scene geometry is provided by digital surface models (DSMs), which are integrated into an automated simulation processing chain. Using strategy 1 (based on building fill ratio, BFR), building changes are detected based on change ratios considering layover and shadow areas. Strategy 2 (based on wall fill position, WFP) enables one to analyze individual facades of buildings without clear decision from strategy 1, which is based on a geometric projection of facade layover pixels. In a case study (Munich city center), the sensitivity of the change-detection methods is exemplified with respect to destroyed buildings and partly changed buildings. The results confirm the significance of integrating prior knowledge from DSMs into the analysis of high-resolution SAR images.