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A Comparison of Real and Simulated Airborne Multisensor Imagery

机译：真实和模拟机载多传感器图像的比较

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This paper presents a methodology and results for the comparison of simulated imagery to real imagery acquired with multiple sensors hosted on an airborne platform. The dataset includes aerial multi- and hyperspectral imagery with spatial resolutions of one meter or less. The multispectral imagery includes data from an airborne sensor with three-band visible color and calibrated radiance imagery in the long-, mid-, and short-wave infrared. The airborne hyperspectral imagery includes 360 bands of calibrated radiance and reflectance data spanning 400 to 2450 nm in wavelength. Collected in September 2012, the imagery is of a park in Avon, NY, and includes a dirt track and areas of grass, gravel, forest, and agricultural fields. A number of artificial targets were deployed in the scene prior to collection for purposes of target detection, subpixel detection, spectral unmixing, and 3D object recognition. A synthetic reconstruction of the collection site was created in DIRSIG, an image generation and modeling tool developed by the Rochester Institute of Technology, based on ground-measured reflectance data, ground photography, and previous airborne imagery. Simulated airborne images were generated using the scene model, time of observation, estimates of the atmospheric conditions, and approximations of the sensor characteristics. The paper provides a comparison between the empirical and simulated images, including a comparison of achieved performance for classification, detection and unmixing applications. It was found that several differences exist due to the way the image is generated, including finite sampling and incomplete knowledge of the scene, atmospheric conditions and sensor characteristics. The lessons learned from this effort can be used in constructing future simulated scenes and further comparisons between real and simulated imagery.
机译：本文介绍了一种方法和结果，用于将模拟图像与通过机载平台上托管的多个传感器获取的真实图像进行比较。该数据集包括空间分辨率为一米或更小的航空多光谱和高光谱图像。多光谱图像包括来自机载传感器的数据，该传感器具有三波段可见色以及在长波，中波和短波红外中的经过校准的辐射度图像。机载高光谱图像包括360个波段的校准辐射度和反射率数据，其波长范围为400至2450 nm。该图像采集于2012年9月，位于纽约州埃文市的一个公园内，包括一条土路以及草地，砾石，森林和农业领域。在收集之前，为了进行目标检测，子像素检测，光谱解混和3D对象识别，在场景中部署了许多人造目标。罗彻斯特理工学院基于地面测量的反射率数据，地面摄影和先前的机载图像，在DIRSIG中创建了收集地点的综合重建物，DIRSIG是一种图像生成和建模工具。使用场景模型，观察时间，大气条件的估计以及传感器特性的近似值生成模拟的机载图像。本文提供了经验图像和模拟图像之间的比较，包括对分类，检测和解混应用的已实现性能的比较。结果发现，由于生成图像的方式不同，存在一些差异，包括有限采样以及对场景，大气条件和传感器特性的不完全了解。从这项工作中学到的经验教训可用于构建未来的模拟场景，以及在真实影像和模拟影像之间进行进一步的比较。

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