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首页> 外文会议>Conference on Image and Signal Processing for Remote Sensing VIII, Sep 24-27, 2002, Agia Pelagia, Crete, Greece >Extraction of visible-SWIR hyperspectral scene statistics and their use in scene realization
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Extraction of visible-SWIR hyperspectral scene statistics and their use in scene realization

机译：可见SWIR高光谱场景统计数据的提取及其在场景实现中的应用

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摘要

A method for the extraction of spectral and spatial scene statistics from hyperspectral data is discussed. The method is designed to work on atmospherically compensated data in any spectral region, although this paper will report on visible scene statistics derived from atmospherically compensated AVIRIS data. Our approach is based on a physical description where the scene is composed of materials that in turn are described by a set of spectral endmembers. The spatial statistics of individual scene materials have more stationary behavior than the statistics for the whole scene. For this reason we have formulated our approach around statistics that are determined from the fractional abundance images obtained from the spectral un-mixing of the scene. These quantities are used to construct a high spatial resolution reflectance or emissivity/temperature surface using a fast autoregressive texture generation tool. The spectral complexity of the synthetic surfaces have been evaluated by inserting objects for detection and calculating ROC curves. Preliminary results indicate that synthetic scenes with realistic levels of spectral clutter can be generated using spectral and spatial statistics determined from endmember fractional abundance maps. This work is motivated by the need for realistic hyperspectral scene generation capabilities to test future hyperspectral sensor concepts.
机译：讨论了一种从高光谱数据中提取光谱和空间场景统计数据的方法。该方法旨在处理任何光谱区域的大气补偿数据，尽管本文将报告从大气补偿AVIRIS数据得出的可见场景统计数据。我们的方法基于物理描述，其中场景由依次由一组光谱端成员描述的材料组成。与整个场景的统计相比，单个场景材料的空间统计具有更多的平稳行为。因此，我们围绕统计数据制定了我们的方法，这些统计数据是根据从场景的光谱解混获得的分数丰度图像确定的。这些数量用于使用快速自回归纹理生成工具构建高空间分辨率反射率或发射率/温度表面。通过插入用于检测和计算ROC曲线的对象，可以评估合成表面的光谱复杂性。初步结果表明，可以使用从端成员分数丰度图确定的光谱和空间统计信息来生成具有逼真的光谱杂波水平的合成场景。这项工作的动力是需要现实的高光谱场景生成功能来测试未来的高光谱传感器概念。

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