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Hyperspectral Sensors as a Management Tool to Prevent the Invasion of the Exotic Cordgrass Spartina densiflora in the Doñana Wetlands

机译:高光谱传感器作为一种管理工具,可防止多纳纳湿地的外来脐带草Spartina densiflora入侵

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摘要

We test the use of hyperspectral sensors for the early detection of the invasive dense-flowered cordgrass ( Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in an area with presence of . We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate detection in the area. We tested several statistical signal detection algorithms implemented in ENVI software as spectral target detection techniques (matched filtering, constrained energy minimization, orthogonal subspace projection, target-constrained interference minimized filter, and adaptive coherence estimator) and compared them to the well-known spectral angle mapper, using spectra extracted from ground-truth locations in the images. The target was easy to detect in the marshes by all algorithms in images of both sensors. The best methods (adaptive coherence estimator and target-constrained interference minimized filter) on the best sensor (AHS) produced 100% discrimination (Kappa = 1, AUC = 1) at the study site and only some decline in performance when extrapolated to a new nearby area. AHS outperformed CASI in spite of having a coarser spatial resolution (4-m vs. 1-m) and lower spectral resolution in the visible and near-infrared range, but had a better signal to noise ratio. The larger spectral range of AHS in the short-wave and thermal infrared was of no particular advantage. Our conclusions are that it is possible to use hyperspectral sensors to map the early spread in the Guadalquivir River marshes. AHS is the most suitable airborne hyperspectral sensor for this task and the signal processing techniques target-constrained interference minimized filter (TCIMF) and adaptive coherence estimator (ACE) are the best performing target detection techniques that can be employed operationally with a simplified processing of hyperspectral images.
机译:我们测试使用高光谱传感器对西班牙西南部瓜达尔基维尔河沼泽地中的侵入性密集开花的香茅(勃朗州)进行早期检测。我们在有的区域串联飞行CASI-1500(368–1052 nm)和AHS(430–13,000 nm)机载传感器。我们简化了高光谱数据的处理(无需大气校正和数据缩减技术),以测试是否需要进行这些处理才能对该区域进行准确检测。我们测试了ENVI软件中作为频谱目标检测技术实现的几种统计信号检测算法(匹配滤波,约束能量最小化,正交子空间投影,目标约束干扰最小化滤波器和自适应相干估计器),并将它们与众所周知的频谱角度进行了比较映射器,使用从图像中真实位置提取的光谱。通过两个传感器图像中的所有算法,可以轻松地在沼泽地中检测目标。最佳传感器(AHS)上的最佳方法(自适应相干估计器和目标约束干扰最小化滤波器)在研究现场产生了100%的辨别力(Kappa = 1,AUC = 1),并且推断出新的方法后性能只会下降附近区域。尽管在可见光和近红外范围内具有较粗的空间分辨率(4-m对1-m)和较低的光谱分辨率,但AHS仍优于CASI,但信噪比更好。在短波和热红外中,AHS的较大光谱范围没有特别的优势。我们的结论是,可以使用高光谱传感器来绘制瓜达尔基维尔河沼泽地的早期扩散图。 AHS是最适合此任务的机载高光谱传感器,并且信号处理技术目标约束干扰最小化滤波器(TCIMF)和自适应相干估计器(ACE)是性能最好的目标检测技术,可在操作中使用高光谱的简化处理图片。

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