The use of very high resolution (VHR) satellite imagery is steadily increasing in many fields, from precision mapping to location-aware businesses. The trend toward finer and finer resolution is pushed by peculiar applications, whose spatial requirements cannot be met even by high resolution sensors. Among them, urban remote sensing and urban-related applications are among the front-runners. However, it is true that VHR data come with equally numerous limits and challenges than advantages and improvements. Of course, this is true for both radar and optical images, but in this chapter we will focus on the optical data, while in a companion chapter radar images will be considered. Indeed, VHR optical imagery is nowadays offered by many sensors, from Quickbird to Worldview-1, from Ikonos to Geoeye-1, from Cartosat-1 to the EROS constellations. Moreover, future systems such as the French Pleiades constellation will provide faster revisit times, thus enhancing the timeliness of the data for urban disaster management and similar applications. A very detailed analysis of the current situation of VHR sensors for urban applications may be found in Ehlers (2009) and won’t be repeated here. It is worth noting, however, that less than 1 meter spatial resolution and less than 1 day repeat images are to be considered a very close goal. The question we would like to address here is whether the users (all, and not only “end-users”) are really able to exploit the wealth of information coming from these sensors. As researchers in remote sensing, and especially in urban remote sensing, are aware, the answer is generally negative. The reasons are not only, however, due to the lack of knowledge by the users of the potentials of these data, but also the problems and various issues related to VHR imagery. According to what is available in technical literature, we will attempt in the following sections to address to what extent these issues are critical, and which is their impact on urban remote sensing and urban area applications. To this aim, we will follow this itemized list: • Issues related to geometrical problems of optical VHR data: all the challenges coming from the geometric accuracy of the data and its positioning in a common reference system. • Issues related to spectral problems, especially the lack of discrimination capability of current VHR sensors and the need of a compromise between VHR in the spectral and the spatial sense. • Issues related to mapping problems, i.e. the need to analyze the scene no more using a per-pixel approach, but gradually shifting to a per-object approach. • Issues related, finally, to multitemporal analysis, e.g. for change detection, whose validity is strongly connected to the ability to correlate features and objects more than isolated pixels. The final part of the chapter will be devoted to provide an example coming form the experience of the authors. The approach proposed in those paragraphs is meant to provide a possible way to overcome some of the problems showed in the first sections. Although it is not the “best” available methodology, it might be useful to highlight one or more interesting and possible research paths and thus invite the interested readers to find their own way to solve any specific urban remote sensing problem of their interest.

Limits and challenges of optical very-high-spatial-resolution satellite remote sensing for urban applications

GAMBA, PAOLO ETTORE;DELL'ACQUA, FABIO;STASOLLA, MATTIA;TRIANNI, GIOVANNA;LISINI, GIANNI
2011-01-01

Abstract

The use of very high resolution (VHR) satellite imagery is steadily increasing in many fields, from precision mapping to location-aware businesses. The trend toward finer and finer resolution is pushed by peculiar applications, whose spatial requirements cannot be met even by high resolution sensors. Among them, urban remote sensing and urban-related applications are among the front-runners. However, it is true that VHR data come with equally numerous limits and challenges than advantages and improvements. Of course, this is true for both radar and optical images, but in this chapter we will focus on the optical data, while in a companion chapter radar images will be considered. Indeed, VHR optical imagery is nowadays offered by many sensors, from Quickbird to Worldview-1, from Ikonos to Geoeye-1, from Cartosat-1 to the EROS constellations. Moreover, future systems such as the French Pleiades constellation will provide faster revisit times, thus enhancing the timeliness of the data for urban disaster management and similar applications. A very detailed analysis of the current situation of VHR sensors for urban applications may be found in Ehlers (2009) and won’t be repeated here. It is worth noting, however, that less than 1 meter spatial resolution and less than 1 day repeat images are to be considered a very close goal. The question we would like to address here is whether the users (all, and not only “end-users”) are really able to exploit the wealth of information coming from these sensors. As researchers in remote sensing, and especially in urban remote sensing, are aware, the answer is generally negative. The reasons are not only, however, due to the lack of knowledge by the users of the potentials of these data, but also the problems and various issues related to VHR imagery. According to what is available in technical literature, we will attempt in the following sections to address to what extent these issues are critical, and which is their impact on urban remote sensing and urban area applications. To this aim, we will follow this itemized list: • Issues related to geometrical problems of optical VHR data: all the challenges coming from the geometric accuracy of the data and its positioning in a common reference system. • Issues related to spectral problems, especially the lack of discrimination capability of current VHR sensors and the need of a compromise between VHR in the spectral and the spatial sense. • Issues related to mapping problems, i.e. the need to analyze the scene no more using a per-pixel approach, but gradually shifting to a per-object approach. • Issues related, finally, to multitemporal analysis, e.g. for change detection, whose validity is strongly connected to the ability to correlate features and objects more than isolated pixels. The final part of the chapter will be devoted to provide an example coming form the experience of the authors. The approach proposed in those paragraphs is meant to provide a possible way to overcome some of the problems showed in the first sections. Although it is not the “best” available methodology, it might be useful to highlight one or more interesting and possible research paths and thus invite the interested readers to find their own way to solve any specific urban remote sensing problem of their interest.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/249299
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