## 3 Literature Review and Comparison of Interferometric Coherence and Intensity Correlation
### Improvement of SAR Data Based Damage Assessment Techniques by Additional Data  
Several authors reported a significant improvement of the damage assessment accuracy by using other data additional to pre- and post-event SAR data such as optical imagery and GIS data (e.g., administrative information, city parcel boundaries, _etc._). [PERSON] _et al._[7] used for the damage assessment of the 1999 Izmit Earthquake, Turkey, the coherence, intensity change and correlation calculated from ERS SAR images and combined them with optical imagery of the IRS satellite. Thereby, they were able to increase the overall accuracy (Kappa coefficient) of the damage assessment approach from 48.4% (0.35) or 54.8% (0.38) (based on only SAR coherence or SAR intensity correlation, respectively) to 88.6% (0.83) (combined with optical data). The overall accuracy (Kappa coefficient) of the damage assessment using only the optical data was 81.9% (0.81). As reference ground truth data from a macroseismic survey campaign was used. For the 2003 Bam Earthquake, Iran, the authors also used SAR intensity correlation and interferometric coherence for damage mapping. Thereby, the damage map produced by the International Charter of Space and Major Disasters [11] (based on optical Satellite Pour l'Observation de la Terre (SPOT), IRS and IKONOS imagery) was used to derive training data for the developed SAR data-based damage assessment procedure. [PERSON] _et al._[78] reported an increase of the overall accuracy (Kappa coefficient) of their damage area detection methods from 60% to 61% (0.36-0.46) (using only SAR data) up to 77%-89% (0.59-0.83) (fusing SAR and optical imagery). For comparison, the accuracy of the damage assessment method using only the high spatial resolution optical data was 70%-82% (0.46-0.73). The lower accuracy numbers are valid for the Bam 2003 Earthquake using ENVISAT ASAR and the optical ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery (15 m spatial resolution), while the higher accuracy values were reported for the case study of the 1999 Izmit Earthquake, Turkey, using ERS-1/2 SAR and IRS1-C optical imagery (5 m spatial resolution). For the aforementioned approach based on both SAR intensity correlation and interferometric coherence for damage assessment of the 2003 Bam Earthquake, Iran, by [PERSON] _et al._[8] (_cf._ Section 3.3), the overall accuracy was 52%. It could be increased to _ca._ 56% by excluding vegetated areas using optical ASTER imagery. For earthquake risk assessment in Teheran, Iran, [PERSON] _et al._[125] used additional to SAR images GIS data such as building height information. Investigating the 2003 Bam Earthquake, Iran, [PERSON] _et al._[94] developed a rapid earthquake damage assessment approach for urban areas by combining pre- and post-disaster ENVISAT ASAR imagery with GIS data (_i.e._, administrative city parcel information). The validation of the proposed method was based on ground survey information. With the additional use of GIS data the user's accuracy (_i.e._, the percentage of correctly detected damaged areas) was considerably increased from _ca._ 19%-46% (based on only the SAR data) to _ca._ 66%-78% (with GIS data). [PERSON] and [PERSON] [2] executed a statistical analysis of SAR coherence and intensity to differ between damaged and undamaged buildings at the block level. Their procedure uses neuro-fuzzy per-pixel and Markov random field spatial model classifiers. The authors investigated the 2003 Boumerdes, Algeria, and the 2007 Pisco, Peru, Earthquakes using ERS-2 and ALOS/PLASAR data, respectively. The damage assessment procedure proposed by [PERSON] and [PERSON] [2] is supported by ancillary data such as GIS layers of city parcels borders in urban area. Thereby, the overall accuracy was increased from _ca._ 63% (based on SAR data) to _ca._ 81% (additional use of GIS data). Possible sources for this geodata are (I) local GIS, (II) Internet maps or (III) interpretation and classification of pre-disaster optical imagery.