URBAN AREA PRODUCT SIMULATION FOR THE ENMAP HYPERSPECTRAL SENSOR P. Gamba † , A. Villa ⋆,⋄ , A. Plaza • , J. Chanussot ⋆ , J.A. Benediktsson ⋄ † Department of Electronics, University of Pavia - Pavia, Italy ⋆ GIPSA-lab, Signal & Image Dept., Grenoble Institute of Technology - INPG, France. ⋄ Faculty of Electrical and Computer Engineering, University of Iceland - Iceland. • Dept. of Technology of Computers and Communications, University of Extremadura - Spain ABSTRACT Low spatial resolution is a major limitation for remote sensing classification, especially in a urban environment. In this work, we will focus on the simulation of urban area environment at a low spatial resolution, comparable to the new hyperspectral sensors that will be launched in the next few years. The aim is to better understand the possibility offered by the new sensors, in a challenging scenario like the one represented by a highly mixed image. Particular attention is placed on the characteris- tics of the sensor EnMap, produced by DLR. The experiments conducted on a real data set confirm the challenges posed by low spatial resolution when analyzing a urban environment. 1. INTRODUCTION Urban areas are currently the most rapidly changing types of land covers, even though they represent only a low percentage of the global land surface [1]. The possibility to monitor these areas is therefore one of the most relevant issues concerning the evaluation of the human impact on the environment. For this purpose, the use of satellite remote sensing imagery can provide a timely and relatively cheap view of urban land covers, as well as a tool to monitor changes in urbanizing landscapes. The most common approach for characterizing urban areas using remote sensing imagery is the land-cover classification, that is assignment of an area to a class which corresponds to the material covering the area or the main purpose for which the land is used. However, the remote sensing characterization of urban environments can be com- plicated for several reasons: (i) usually, urban land-cover classes are not well spectrally distinct, resulting in consider- able confusion between classes [2], (ii) the physical structure of land-use classes varies from site to site due to the different roofing and paving materials and building typology [1], (iii) urban areas are heterogeneous and most pixels, at the satellite spatial resolution which can vary from few to tens of meters This work has been in part supported by the European Community’s Marie Curie Research Training Networks Programme under contract MRTN- CT-2006-035927, Hyperspectral Imaging Network (HYPER-I-NET). per pixel, appear mixed with varying proportions of different components and/or materials [3]. The use of hyperspectral sensors for land cover monitoring is receiving continuosly growing attention due to the advanta- geous characteristic of such data and to the already planned civilian space missions which will make available in the next future a huge quantity of hyperspectral data (amongst the others, PRISMA, planned by the Italian Space Agency ASI in 2014, EnMap planned by German Space Agency DLR in 2014, Hyper-J and HyspIRI, planned respectively bt the Japan Space Agency and NASA in the next future, besides the already on orbit sensors like the widely used Hyperion and AVIRIS, both of NASA). The potentialities offered by the new generation of hyperspectral satellite imagery for urban applications is a challenging aspect that this paper intends to deal with, as it is still not fully investigated [4]. In this paper, particular attention is placed on the characteristics of the sensor EnMap, produced by DLR [5, 6]. The remainder of the paper is as follows. Section 2 introduces the characteristics of the EnMap sensor. Section 3 describes the experimental design followed in this work, while Sections 4 and 5 present a discussion of the results and the conclusions of the work. 2. CHARACTERISTICS OF THE ENMAP SENSOR The Environmental Mapping and Analysis Program (En- MAP) German hyperspectral mission is intended to provide new quality spectral information about the Earth surface, ex- ploiting the state-of-the-art hyperspectral sensor technology. The sensor is designed to acquire areas of 30 × 30 km with a ground sampling distance (GSD) of 30 m, measuring in the 420-2450 nm spectral range with more than 240 bands by means of two entirely independent prism-based spec- trometers that cover the spectral regions from the visible to near-infrared (VNIR) and the short-wave infrared (SWIR). The mean spectral sampling interval is 6.5 nm in the VNIR and 10 nm in the SWIR. Despite the modular architecture of the EnMAP scene simu- 1259 978-1-4577-1005-6/11/$26.00 ©2011 IEEE IGARSS 2011