4 research outputs found
Temperature - Emissivity separation assessment in a sub-urban scenario
In this paper, a methodology that aims at evaluating the effectiveness of different TES strategies is presented. The methodology takes into account the specific material of interest in the monitored scenario, sensor characteristics, and errors in the atmospheric compensation step. The methodology is proposed in order to predict and analyse algorithms performances during the planning of a remote sensing mission, aimed to discover specific materials of interest in the monitored scenario. As case study, the proposed methodology is applied to a real airborne data set of a suburban scenario. In order to perform the TES problem, three state-of-the-art algorithms, and a recently proposed one, are investigated: Temperature-Emissivity Separation'98 (TES-98) algorithm, Stepwise Refining TES (SRTES) algorithm, Linear piecewise TES (LTES) algorithm, and Optimized Smoothing TES (OSTES) algorithm. At the end, the accuracy obtained with real data, and the ones predicted by means of the proposed methodology are compared and discussed
Remote sensing satellite image processing techniques for image classification: a comprehensive survey
This paper is a brief survey of advance technological aspects
of Digital Image Processing which are applied to remote
sensing images obtained from various satellite sensors. In
remote sensing, the image processing techniques can be
categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification.
Image pre-processing is the initial processing which deals
with correcting radiometric distortions, atmospheric distortion
and geometric distortions present in the raw image data.
Enhancement techniques are applied to preprocessed data in
order to effectively display the image for visual interpretation.
It includes techniques to effectively distinguish surface
features for visual interpretation. Transformation aims to
identify particular feature of earth’s surface and classification
is a process of grouping the pixels, that produces effective
thematic map of particular land use and land cover
Optimizing Object, Atmosphere, and Sensor Parameters in Thermal Hyperspectral Imagery
We address the problem of estimating atmosphere parameters (temperature and water vapor content) from data captured by an airborne thermal hyperspectral imager and propose a method based on linear and nonlinear optimization. The method is used for the estimation of the parameters (temperature and emissivity) of the observed object as well as sensor gain under certain restrictions. The method is analyzed with respect to sensitivity to noise and the number of spectral bands. Simulations with synthetic signatures are performed to validate the analysis, showing that the estimation can be performed with as few as 10-20 spectral bands at moderate noise levels. The proposed method is also extended to exploit additional knowledge, for example, measurements of atmospheric parameters and sensor noise. Additionally, we show how to extend the method in order to improve spectral calibration.Funding Agencies|Swedish Research Council under Project EMC2</p