Remote sensing application

Abstract: For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Pearson Series Geographic Information Science. Now in full color, the Fourth Edition provides up-to-date information on analytical methods used to analyze digital remote sensing data. Each chapter contains a substantive reference list that can be used by students and scientists as a starting place for their digital image processing project or research. A new appendix provides sources of imagery and other geospatial information.

Abstract: Remote sensing from airborne and spaceborne platforms provides valuable data for mapping, environmental monitoring, disaster management and civil and military intelligence. However, to explore the full value of these data, the appropriate information has to be extracted and presented in standard format to import it into geo-information systems and thus allow efficient decision processes. The object-oriented approach can contribute to powerful automatic and semi-automatic analysis for most remote sensing applications. Synergetic use to pixel-based or statistical signal processing methods explores the rich information contents. Here, we explain principal strategies of object-oriented analysis, discuss how the combination with fuzzy methods allows implementing expert knowledge and describe a representative example for the proposed workflow from remote sensing imagery to GIS. The strategies are demonstrated using the first object-oriented image analysis software on the market, eCognition, which provides an appropriate link between remote sensing imagery and GIS.

Abstract: A wide range of methods for analysis of airborne- and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.

Abstract: Several methods for the analysis of remotely sensed reflectance data are compared, including empirical methods and scattering theories, both of which are important for solving remote sensing problems. The concept of the photon mean optical path length and the implications for use in modeling reflectance spectra are presented. It is shown that the mean optical path length in a particulate surface is in rough inverse proportion to the square root of the absorption coefficient. Thus, the stronger absorber a material is, the less photons will penetrate into the surface. The concept of apparent absorbance (-In reflectance) is presented, and it is shown that absorption bands, which are Gaussian in shape when plotted as absorption coefficient (true absorbance) versus photon energy, are also Gaussians in apparent absorbance. However, the Gaussians in apparent absorbance have a smaller intensity and a width which is a factor of √2 larger. An apparent continuum in a reflectance spectrum is modeled as a mathematical function used to isolate a particular absorption feature for analysis. It is shown that a continuum should be removed by dividing it into the reflectance spectrum or subtracting it from the apparent absorbance and that the fitting of Gaussians to absorption features should be done using apparent absorbance versus photon energy. Kubelka-Munk theory is only valid for materials with small total absorption and for bihemispherical reflectance, which are rarely encountered in geologic remote sensing. It is shown that the recently advocated bidirectional reflectance theories have the potential for use in deriving mineral abundance from a reflectance spectrum.