Loading...
Please wait, while we are loading the content...
A Multi-Sensor Fusion Framework Based On Coupled Residual Convolutional Neural Networks
| Content Provider | MDPI |
|---|---|
| Author | Li, Hao Ghamisi, Pedram Rasti, Behnood Wu, Zhaoyan Shapiro, Aurelie Schultz, Michael Zipf, Alexander |
| Copyright Year | 2020 |
| Description | 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. |
| Starting Page | 2067 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs12122067 |
| Journal | Remote Sensing |
| Issue Number | 12 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-06-26 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Deep Learning Data Fusion Hyperspectral Image Classification Residual Learning Multi-sensor Fusion Convolutional Neural Networks (cnns) Auxiliary Loss Function |
| Content Type | Text |
| Resource Type | Article |
