A Multiple Remote Sensing Sensor Fusion System Using Choquet Fuzzy Integral and Modified Particle Swarm Optimization (FI
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RESEARCH ARTICLE
A Multiple Remote Sensing Sensor Fusion System Using Choquet Fuzzy Integral and Modified Particle Swarm Optimization (FI-MPSO) Behnaz Bigdeli1 • Parham Pahlavani2 • Hamed Amini Amirkolaee2 Received: 31 December 2019 / Accepted: 21 October 2020 Indian Society of Remote Sensing 2020
Abstract The simultaneous use of remote sensing sensors has been of great interest due to reasons such as the weaknesses of each sensor individually and the improvement in results by integrating multisensor information. In this context, decision fusion of multiremote sensing sensors is one of the hot research topics for improvement in land cover classification. This paper investigates a multisensor fusion system based on Choquet fuzzy integral and a modified particle swarm optimization for fusion of high-resolution visible RGB image and thermal infrared hyperspectral. Instead of using common optimization algorithms, a new version of PSO is used to reduce the running time and to increase the accuracy. After some processes and fuzzy classifications, a fuzzy integral–fuzzy measure fusion methodology which uses modified PSO to find the best subset of fuzzy measures is utilized to fuse the resulted decision profiles of soft decision-making system. Also, comparison between proposed method and other successful decision fusion techniques such as Dempsters shafer, AdaBoost and Bagging is investigated. Experiments are executed on high-resolution RGB image and thermal infrared hyperspectral data from Quebec of Canada. The results show that the suggested method obtains 92% of overall accuracy and outperforms the other decision fusion and optimization methods in classification performance. Keywords Multisensor fusion Classification Fuzzy integral Modified PSO Fuzzy measures
Introduction Recent advances in remote sensing have produced a variety of sensors with different abilities. These sensors are different in various ways including: collected information, being active or passive and their different spatial, spectral and temporal resolutions. Most of the time, in order to overcome the weaknesses of different sensors and to produce more accurate and consistent information, the fusion & Behnaz Bigdeli [email protected] Parham Pahlavani [email protected] Hamed Amini Amirkolaee [email protected] 1
School of Civil Engineering, Shahrood University of Technology, P.O Box: 3619995161, Shahrood, Iran
2
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, North Kargar Street, Tehran, Iran
of data from different sensors is considered (Bigdeli and Pahlavani 2016; Shimoni et al. 2011; Zhang et al. 2013; Liao et al. 2014). In recent decades, numerous studies have been conducted on the fusion of remote sensing sensors. These researches are different in terms of the type of fused sensors, the desired applications and the methods of fusion. Using of different remote sensing sensors is an important aspect in investigating of multisensory fusion researches. Most researches in this area
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