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首页> 外文会议>Geoscience and Remote Sensing Symposium, 2007 IEEE International >Vegetation classification using hyperspectral and multi-angular remote sensing data
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Vegetation classification using hyperspectral and multi-angular remote sensing data

机译：利用高光谱和多角度遥感数据进行植被分类

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In this study, vegetation cover type classification was investigated using CHRIS data over agricultural scenes acquired across the 2004 growing season. Spectral indices sensitive to crop chlorophyll content and leaf area index were first calculated from CHRIS nadir data in May, June and July. The seasonality of these indices was analyzed and employed to identify crop types in the study area. To further improve the classification accuracy, the angular signatures of the vegetation canopies were derived from the nadir and off- nadir data in the red and near-infrared band using the kernel-driven Ross_Thick and Li-Sparse model. The coefficients of the kernel-based model were then used for crop type classification, together with the spectral indices derived from nadir data. Preliminary results show that the additional angular information can slightly improve the classification accuracy.
机译：在这项研究中，使用CHRIS数据调查了2004年整个生长季节获得的农业场景的植被覆盖类型分类。首先从5月，6月和7月的CHRIS天底数据中计算出对作物叶绿素含量和叶面积指数敏感的光谱指数。对这些指数的季节性进行了分析，并用于确定研究区域的作物类型。为了进一步提高分类精度，使用核驱动的Ross_Thick和Li-Sparse模型从红色和近红外波段的最低和最低数据导出了植被冠层的角度特征。然后将基于核的模型的系数与从最低点数据得出的光谱指数一起用于作物类型分类。初步结果表明，附加的角度信息可以稍微提高分类精度。

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