• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of High Speed Electronics and Systems: Devices, Integrated Circuits and Systems, Optical and Quantum Electronics >DETECTION OF GASEOUS EFFLUENTS FROM AIRBORNE LWIR HYPERSPECTRAL IMAGERY USING PHYSICS-BASED SIGNATURES
【24h】

DETECTION OF GASEOUS EFFLUENTS FROM AIRBORNE LWIR HYPERSPECTRAL IMAGERY USING PHYSICS-BASED SIGNATURES

机译：基于物理特征的空中LWIR高光谱成像中气体排放的检测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and the concentration of the gas. All of these quantities vary spatially further complicating the detection problem. In complex scenes simple estimation of a "residual" spectrum may not be possible due to the variability in the scene background. A common detection scheme uses a matched filter formalism to compare laboratory-measured gas absorption spectra with measured pixel radiances. This methodology can not account for the variable signature strengths due to concentration path length and temperature contrast, nor does it take into account measured signatures that are observed in both absorption and emission in the same scene. We have developed a physics-based, forward model to predict in-scene signatures covering a wide range in gas/surface properties. This target space is reduced to a set of basis vectors using a geometrical model of the space. Corresponding background basis vectors are derived to describe the non-plume pixels in the image. A Generalized Likelihood Ratio Test is then used to discriminate between plume and non-plume pixels. Several species can be tested for iteratively. The algorithm is applied to airborne LWIR hyperspectral imagery collected by the Airborne Hyperspectral Imager (AHI) over a chemical facility with some ground truth. When compared to results from a clutter matched filter the physics-based signature approach shows significantly improved performance for the data set considered here.
机译：从机载平台检测气态烟羽向遥感界提出了独特的挑战。测得的特征是现象学的复杂组合，包括大气的影响，羽流下背景材料的光谱特性，气体与表面之间的温度对比以及气体浓度。所有这些量在空间上变化，进一步使检测问题复杂化。在复杂的场景中，由于场景背景的可变性，可能无法简单估计“残留”频谱。常见的检测方案使用匹配的过滤器形式来将实验室测量的气体吸收光谱与测量的像素辐射度进行比较。该方法不能考虑由于浓度路径长度和温度对比而导致的可变特征强度，也不能考虑在同一场景中在吸收和发射过程中观察到的实测特征。我们已经开发出基于物理学的正向模型，以预测覆盖广泛气体/表面特性的场景内特征。使用该空间的几何模型将该目标空间缩减为一组基础向量。导出对应的背景基础向量以描述图像中的非软像素。然后使用广义似然比测试来区分羽状像素和非羽状像素。可以反复测试几个物种。该算法适用于由机载高光谱成像仪（AHI）在具有一定地面真实性的化学设施上收集的机载LWIR高光谱图像。当与杂波匹配滤波器的结果进行比较时，基于物理学的签名方法显示了此处考虑的数据集的显着改善的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号