Microsoft Research Audio 104624: A High-level Fusion Method To Fuse Disparate High-resolution Airborne Sensor Data For Change-detection Application - Info and Reading Options
By Microsoft Research
"Microsoft Research Audio 104624: A High-level Fusion Method To Fuse Disparate High-resolution Airborne Sensor Data For Change-detection Application" and the language of the book is English.
“Microsoft Research Audio 104624: A High-level Fusion Method To Fuse Disparate High-resolution Airborne Sensor Data For Change-detection Application” Metadata:
- Title: ➤ Microsoft Research Audio 104624: A High-level Fusion Method To Fuse Disparate High-resolution Airborne Sensor Data For Change-detection Application
- Author: Microsoft Research
- Language: English
Edition Identifiers:
- Internet Archive ID: ➤ Microsoft_Research_Audio_104624
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"Microsoft Research Audio 104624: A High-level Fusion Method To Fuse Disparate High-resolution Airborne Sensor Data For Change-detection Application" Description:
The Internet Archive:
<p>A high-level data fusion system that adopts Bayesian statistics involving weights of evidence modelling is described to combine disparate information from airborne digital data such as a digital surface model (DSM), colour (ADS40), thermal infrared (TABI) and hyperspectral images (AISA) of different period. To show the efficacy of the system, an analysis of subtle change-detection is demonstrated. The data fusion system is capable of detecting changes in man-made features automatically in a densely populated area where there are no much prior information. Multi-class segmented images are obtained from the data captured by the four airborne remote sensing sensors. The system performs data fusion modelling by using binary images of each theme classes and thus a total of 40 binary patterns are obtained.</p><p>Through Bayesian methods, involving weights of evidence modelling, all the binary images are analysed and finally four binary patterns (indicator images) are identified automatically as significant for the change-detection application. A weighted index overlay model available in the system, combines these four patterns. Data fusion by the weights of evidence modelling is found to be candid and unequivocal for predicting newly transformed locations.</p><p>Results of Bayesian method are accurate as the weights are based on statistical analysis. Changes in features such as colour of roof, parking areas, openland areas, newly built structures, and presence or absence of vehicle is automatically extracted by using the high-level data fusion approach. The final predictor image showing the probability of change-detected areas in a high-dense city of Japan is generated robustly.</p><p>The fused image products can be used to update maps for digital cartographic applications.</p><div class="fs30">©2005 Microsoft Corporation. All rights reserved.</div>
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- Added Date: 2013-11-24 03:05:59
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