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AN INVERSE RADIATIVE TRANSFER MODEL TO EXTRACT GROUND SPECTRAL REFLECTANCE OF URBAN AREAS

Automatic mapping of urban materials from remotely sensed radiance images remains difficult because of the complex phenomena induced by relief. Indeed, shadows and environment effects disrupt the radiance incoming the sensor. The measured radiance depends on the illumination conditions of the observed area. This study proposes a physical model able to solve the radiative transfer inversion problem in urban areas, in the reflective domain (0.4 μm – 2.5 μm), from high spatial and spectral resolution images. This new approach takes into account the relief, the spatial heterogeneity of the scene and atmospheric effects, in order to extract rigorously the ground surface reflectance. The method consists in modelling separately the irradiance and radiance components at ground and sensor levels. The validity of the various assumptions introduced in this model is checked through comparisons with Amartis, which is considered as a reference direct radiative transfer code. The results obtained over different typical urban scene and for various wavelengths show an absolute error of less than 0.01 on the retrieved reflectance after the inversion process. A field campaign was also carried out in Toulouse (France) in order to validate the model in real conditions. Measurements were carried out using two airborne Pelican sensors consisting in 8 high resolution cameras. The main results are that the estimated reflectances from the same material in sunny and shadowed areas exhibiting no bias. It means in particular that the topographic and environment effects are accurately corrected. Furthers, the main close error comes from the misregistration between the DEM and the radiometric images. INTRODUCTION Recent studies estimate that more than 75% of the world population are living in urban areas. Many applications required a better understanding of this environment to analyse its development. With the advent of high spatial resolution sensors in the visible and near infrared domain [0.4 μm – 2.5 μm], it becomes possible to obtain precise information from airborne or satellite images. Due to their highly structured geometry, an important part of urban areas exhibits shadows. Thus, urban acquisitions will depend on the illuminations conditions. This drawback must be overcome to improve applications as mapping or urban change detection, which often use images from different years or different sensors. As the urban environment makes it difficult to classify automatically urban materials from airborne radiance measurements, preprocessing algorithms are usually used to extract ground surface intrinsic properties like reflectance that is independent of the irradiance conditions. Currently, different methods are used to solve the inverse transfer radiative problems (i.e. retrieving ground reflectance from radiance images). Most of these codes consider a flat and homogeneous landscape and a lambertian assumption, e.g., ATREM (i), ACORN (ii). Improvements have 1st EARSeL Workshop of the SIG Urban Remote Sensing Humboldt-Universität zu Berlin, 2-3 March 2006 2 been carried out in FLAASH and COCHISE as they consider a heterogeneous background. Nevertheless, these codes are not able to deal with urban areas where the relief plays very important part in the remotely sensed features. Two models currently take into account the ground topography: ATCOR4 (iii) and SIERRA (iv), but these codes are not adapted to the very uneven structure of urban areas, notably in shadows, where the earth-atmosphere coupling phenomena are still roughly modelled. The need for a radiative transfer model that is able to understand and model the signal over urban areas led us to develop the method we report here. This method consists in modelling separately the irradiance and radiance components at ground and sensor level by use of models derived principally from improved versions of the usual spatially resolved approaches. Each component takes into account the relief, ground heterogeneity and atmospheric corrections. First, in order to check the validity of the different assumptions made, an end-to-end method is applied using the reference radiative transfer model Amartis (v), performed over a typical urban scene. Then, to complete this theoretical validation, a field campaign is carried out to validate our model on real images and first results are presented. RADIATIVE TRANSFER MODELLING OVER URBAN AREAS Considering an airborne or satellite sensor observing an urban scene in the visible and near infrared domain [0.4 μm – 2.5 μm] and i a pixel of the sensor image, the total radiance received by this sensor can be written as the sum of three main contributors (Figure 1) : (i) R (i) R (i) R (i) R direct c atmospheri diffuse sensor + + = (1) The first component corresponds to scattered photons coming from the target neighbouring (Rdiffuse). Ratmospheric describes photons coming to the sensor without reaching the surface.