刘瑶

 Introduction  Method  Experiment results  Summary & future work

 Definition of image simulation ◦ generates synthetic images based on the analysis and understanding of imaging acquisition  Application ◦ Evaluation of system specifications ◦ Test of processing facilities ◦ Test-bench for future algorithm development ◦ Cost-versus-quality trade-offs Image simulation Mid-infrared absorption bands Purpose

 Simulation tools ◦ DIRSIG (The Digital Imaging and Remote Sensing Image Generation Model) Spectral range: μm region Types of imagery: multi- and hyper-spectral passive systems, polarimetric imagery, radiative transfer in littoral waters, and active LIDAR systems source ： scene-building-with-blender.html

 Simulation tools ◦ EeTes (EnMAP end-to-end Simulation)  Spectral range: VNIR & SWIR ◦ PICASSO (Parameterized Image Chain Analysis & Simulation SOftware)  Spectral Range: visible to near-infrared(VISNIR) & TIR  Summary ◦ Image simulation in mid-infrared regions is rarely discussed, especially the absorption bands.

 Applications of mid-infrared regions (3-5 μm) ◦ Sensitive to high temperature objects(fire, active volcanoes etc.)  Mid-infrared absorption bands ◦ Fundamental research on these two special band to make preparation for mid-infrared simulation. Image simulation Mid-infrared absorption bands Purpose

 Image simulation chain ◦ Surface scene simulation is basis for other two processes. ◦ Solar radiation is absorbed and less will reach the ground and be reflected.  Question ◦ whether the reflected part of surface radiance can be neglected ? ◦ what factors affect the surface radiance composition ?  study bands: 2.7 &4.3 μm Surface scene simulation Atmospheric simulation Sensor hardware simulation Image simulation Mid-infrared absorption bands Purpose

 ground radiance simualtion ◦ atmospheric transfer model MODTRAN (MODerate resolution atmospheric TRANsmission) ◦ MODTRAN can simulate the absorption effects of atmospheric molecules to the solar radiation.  Simulation outcome ◦ Total surface radiance (represented by Rt)  Reflected radiance ( represented by Rr)  Emitted radiance (represented by Re)  Rt = Rr +Re ◦ Evaluation index: Rr / Re

 Input parameters atmosphere type mid latitude summer/winter aerosol typeurban visibility50 kilometers solar zenith angle30° view zenith angle30° relative azimuth angle90° surface temperature300K/272.2K gas concentration (H 2 O,O 3,CO 2 ) default values sensor altitude1m surface altitude0

 surface features  assume all features are lambert in simulation. Type of objectsName vegetation conifer deciduous grass soil sandy loam brown fine sandy loam brown loamy fine sand water sea water distilled water

 Spectral reflectance (from JHU spectral library) The reflectance of soil is relatively higher than vegetation and water

 Rr/Re near 2.7μm in summer and winter Temperature & reflectance have impacts on surface radiance compositon in mid-infrared absorption bands

 Rr/Re near 4.3μm in summer and winter The result is similar to that in 2.7 μm regions

 Ratio of Rr to Re of the band ◦ assumption: square-wave spectral response function  Response equals 1 within the band  Response equals 0 outside the band

 Rr_b/Re_b in 2.7 & 4.3 band The result in bands is consistent with that in wavelengths.

 Summary ◦ Temperature and reflectance of surface features both contribute to the surface radiance composition. ◦ Whether the reflected radiance can be neglected in surface scene simulation relates to the expected accuracy of simulation. For example, if a 10 percent of error is allowed, the reflection of soils, water and vegetation can all be neglected.

 Further work ◦ More factors need to be involved: water vapor contents, BRDF, etc. ◦ Reflectance data of surface features should be expanded. ◦ In-situ validation: field measurements of reflected and emitted radiance. ◦ Simulation is working with the sensor. Since the proportion changes with the wavelength, for specific sensor, the surface composition analysis also depends on the bandwidth.