A multi-sensor fusion framework based on coupled residual convolutional neural networks
Multi-sensor remote sensing image classification has been considerably improved by deep learning feature extraction and classification networks. In this paper, we propose a novel multi-sensor fusion framework for the fusion of diverse remote sensing data sources. The novelty of this paper is grounde...
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| Hauptverfasser: | , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
26 June 2020
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| In: |
Remote sensing
Year: 2020, Jahrgang: 12, Heft: 12 |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs12122067 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3390/rs12122067 Verlag, lizenzpflichtig, Volltext: https://www.mdpi.com/2072-4292/12/12/2067 
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| Verfasserangaben: | Hao Li, Pedram Ghamisi, Behnood Rasti, Zhaoyan Wu, Aurelie Shapiro, Michael Schultz and Alexander Zipf |
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| 520 | |a Multi-sensor remote sensing image classification has been considerably improved by deep learning feature extraction and classification networks. In this paper, we propose a novel multi-sensor fusion framework for the fusion of diverse remote sensing data sources. The novelty of this paper is grounded in three important design innovations: 1- a unique adaptation of the coupled residual networks to address multi-sensor data classification; 2- a smart auxiliary training via adjusting the loss function to address classifications with limited samples; and 3- a unique design of the residual blocks to reduce the computational complexity while preserving the discriminative characteristics of multi-sensor features. The proposed classification framework is evaluated using three different remote sensing datasets: the urban Houston university datasets (including Houston 2013 and the training portion of Houston 2018) and the rural Trento dataset. The proposed framework achieves high overall accuracies of 93.57%, 81.20%, and 98.81% on Houston 2013, the training portion of Houston 2018, and Trento datasets, respectively. Additionally, the experimental results demonstrate considerable improvements in classification accuracies compared with the existing state-of-the-art methods. | ||
| 650 | 4 | |a auxiliary loss function | |
| 650 | 4 | |a convolutional neural networks (CNNs) | |
| 650 | 4 | |a data fusion | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a hyperspectral image classification | |
| 650 | 4 | |a multi-sensor fusion | |
| 650 | 4 | |a residual learning | |
| 700 | 1 | |a Ghamisi, Pedram |e VerfasserIn |4 aut | |
| 700 | 1 | |a Rasti, Behnood |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Shapiro, Aurelie |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Zipf, Alexander |d 1971- |e VerfasserIn |0 (DE-588)123246369 |0 (DE-627)082437076 |0 (DE-576)175641056 |4 aut | |
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