Tunnel-Site Selection by Remote Sensing Techniques
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Abstract : A study of the role of remote sensing for geologic reconnaissance for tunnel-site selection was commenced. For this study, remote sensing was defined as ultraviolet to thermal infrared multispectral scanning, X- and L-band synthetic aperture radar, and aerial photography. Data from these sensors were processed and evaluated in terms of their complementary use. This report can be used tutorially on the data processing and basic instrumentation of conventional remote sensing. Future research directions are suggested, and the extension of remote sensing to include airborne passive microwave sensor systems, magnetometry, gamma-ray sensors, gravimetry, and airborne electromagnetic sounding systems is discussed.Keywords:
Instrumentation
Aerial photography
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With the development of the Global Position System (GPS) and inertial navigation systems (INS), the precise position and attitude parameters of an airborne platform can be obtained. The new system, such as INSAR and Scanning Laser Ranger (SLR), are excellent equipment for acquiring the relative height of Earth objects. At the same time optical remote sensing sensors are making rapid progress. It is time to integrate these system into a new one, which can provide high accurate DEM and remote sensing image synchronically.
Position (finance)
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Earth remote sensing
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Imaging spectrometry, a new technique for the remote sensing of the earth, is now technically feasible from aircraft and spacecraft. The initial results show that remote, direct identification of surface materials on a picture-element basis can be accomplished by proper sampling of absorption features in the reflectance spectrum. The airborne and spaceborne sensors are capable of acquiring images simultaneously in 100 to 200 contiguous spectral bands. The ability to acquire laboratory-like spectra remotely is a major advance in remote sensing capability. Concomitant advances in computer technology for the reduction and storage of such potentially massive data sets are at hand, and new analytic techniques are being developed to extract the full information content of the data. The emphasis on the deterministic approach to multispectral data analysis as opposed to the statistical approaches used in the past should stimulate the development of new digital image-processing methodologies.
Earth observation
Identification
Remote sensing application
Data Processing
Multispectral pattern recognition
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The authors have studied remote sensing data from sensors in different wavelength regions (optical, thermal infrared and microwave) and from different platforms (airborne and spaceborne) in order to extract geographical information. By comparing the extracted information with an existing geographical database of a test area in The Netherlands the authors find that to obtain relevant cartographic information from remote sensing images resolutions of 5 meter or less are required. For appropriate classification of extended objects like agricultural fields multi-layer imagery is necessary.
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Multisensor and multispectral (MS) methods of data fusion related especially to high-resolution or super-resolution remote sensing observations of the oceans are considered. We refer to this topic as ocean remote sensing data fusion (ODF). This is a challenging task which includes analysis, physics-based modeling, simulation, and prediction of MS data. We believe that by using ODF, it will be possible to extract and recognize relevant remote sensing information associated with different oceanic processes. Possible ODF concepts, methodology, and numerical examples are presented and discussed.
Sensor Fusion
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Forest Inventory
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Modern hyper- and ultra- spectral remote sensors are capable of providing spectra with thousands of channels. These channels are not independent of each other. We will analyze the information content of the hyperspectral data using principal component analysis. We will show that the information content of the original spectrum is conserved by Empirical Orthogonal Function (EOF) transformations. A radiative transfer model and a physical inversion algorithm based on principal component analysis will be presented.
Empirical orthogonal functions
Content (measure theory)
Component (thermodynamics)
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Earth Observation through microwave radiometry is particularly useful for various applications, e.g., soil moisture, ocean salinity, or sea ice cover. However, most of the image processing/data analysis techniques aiming to provide automatic measurement from remote sensing data do not rely on any spatial information, similarly to the early years of optical/hyperspectral remote sensing. After more than a decade of research, it has been observed that spatial information can very significantly improve the accuracy of land use/land cover maps. In this context, the goal of this paper is to propose a few insights on how spatial information can benefit to (passive) microwave remote sensing. To do so, we focus here on mathematical morphology and provide some illustrative examples where morphological operators can improve the processing and analysis of microwave radiometric information. Such tools had great influence on multispectral/hyperspectral remote sensing in the past, and are expected to have a similar impact in the microwave field in the future, with the launch of upcoming missions with improved spatial resolution, e.g. SMOS-NEXT.
Radiometry
Earth observation
Land Cover
Microwave Imaging
Remote sensing application
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Advanced information processing and architectures will be needed to bridge the gap between the potential offered by the new generations of sensors and the needs of the end-users to actually face tomorrow's challenges in many applications with a very high societal impact. As remote sensing researchers and engineers, this is our passion, our charge, and our responsibility.
Bridge (graph theory)
Passion
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A new airborne Earth observing system has been developed in China directed by the Expert Group for Information Acquisition and Processing Technology, Hi-tech Research and Development Program of China during the period from 1996 to 2000. The system is composed of 5 remote sensing sensors, including a 128-band modular imaging spectrometer with high spectral resolution, 244-band hyperspectral imaging spectrometer, an area array CCD digital camera with high spatial resolution, a 3 D imager with real time imaging capability, and an L-band SAR with 3m resolution. These sensors are installed onto different aircraft according to their performance, therefore, forming a set of distributive airborne Earth observing system. Since 1998, demonstrative applications study using the system has been conducted, and remote sensing imagery over 10 test sites have been acquired. These data after processing and analysis have achieved very satisfactory application results in rice classification, urban planning, water pollution and desertification monitoring, natural hazards monitoring, mineral exploration and other fields. These demonstrate the roles and application potentials of the new airborne system for Earth observation.
Imaging spectrometer
Earth observation
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