Fontanilles Guillaume, Briottet Xavier
Université de Toulouse, ISAE, 10 Avenue E. Belin, F-31055 Toulouse, France. guillaume.fontanilles@acri‐st.fr
Appl Opt. 2011 Jul 10;50(20):3666-77. doi: 10.1364/AO.50.003666.
This paper presents the physical principle of a new (to our knowledge) unmixing method to retrieve optical properties (reflectance and emissivity) and surface temperatures over a heterogeneous and a folded landscape using hyperspectral and multiangular airborne images acquired with high spatial resolution. In fact, over such a complex scene, the linear mixing model of the reflectance commonly used in the reflective domain is no longer valid in the IR range for the two following reasons: multiple reflections due to the three-dimensional (3D) structure and the radiative phenomenon introduced by the temperature by way of the black body law. Thus, to solve this nonlinear unmixing problem, a new physical model of aggregation is used. Our model requires as inputs knowledge of the 3D scene structure and the spatial contribution of each material in the scene. Each elementary scene element is characterized by its optical properties, and its temperature, spectral, and multiangular acquisitions are required. This paper focuses only on the theoretical feasibility of such a method. In addition, an analysis is conducted evaluating the impact of the misregistration between the radiometric image and its digital terrain model, estimating a threshold of the relative importance of every elementary material to retrieve its corresponding optical properties and temperature. The results show that the 3D geometry must be accurately known (accuracy of 1 m for a spatial resolution of 20 m), and the relative contribution of material in the mixed area must be above 15% to retrieve its surface temperature with an accuracy better than 1 K. So, this method is applied on three different landscapes (heterogeneous flat surface, V shape, and urban canyon), and the results exhibit performances better than 1% for optical properties and 1 K for surface temperatures.
本文介绍了一种新的（据我们所知）解混方法的物理原理，该方法利用高空间分辨率获取的高光谱和多角度航空图像，来反演非均匀和折叠地形上的光学特性（反射率和发射率）及表面温度。事实上，在这样一个复杂场景中，反射领域常用的反射率线性混合模型在红外范围内不再有效，原因如下：三维（3D）结构导致的多次反射以及黑体定律所引入的由温度引起的辐射现象。因此，为了解决这个非线性解混问题，我们使用了一种新的聚集物理模型。我们的模型需要输入3D场景结构以及场景中每种材料的空间贡献信息。每个基本场景元素都由其光学特性来表征，并且需要获取其温度、光谱和多角度信息。本文仅关注该方法的理论可行性。此外，还进行了一项分析，评估辐射图像与其数字地形模型之间配准误差的影响，估计每种基本材料的相对重要性阈值以反演其相应的光学特性和温度。结果表明，必须精确知道3D几何形状（对于20米的空间分辨率，精度为1米），并且混合区域中材料的相对贡献必须高于15%，才能以优于1K的精度反演其表面温度。所以，该方法应用于三种不同的地形（非均匀平面、V形和城市峡谷），结果显示光学特性的性能优于1%，表面温度的性能优于1K。
