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Title: Deep feature fusion with integration of residual connection and attention model for classification of VHR remote sensing images
Authors: Wang, JC 
Shen, L
Qiao, WF
Dai, YS
Li, ZL 
Keywords: Fully convolutional network (FCN)
Very-high-resolution (VHR) image classification
Residual connection
Attention model
Feature fusion
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Remote sensing, 1 July 2019, v. 11, no. 13, 1617, p. 1-22 How to cite?
Journal: Remote sensing 
Abstract: The classification of very-high-resolution (VHR) remote sensing images is essential in many applications. However, high intraclass and low interclass variations in these kinds of images pose serious challenges. Fully convolutional network (FCN) models, which benefit from a powerful feature learning ability, have shown impressive performance and great potential. Nevertheless, only classification results with coarse resolution can be obtained from the original FCN method. Deep feature fusion is often employed to improve the resolution of outputs. Existing strategies for such fusion are not capable of properly utilizing the low-level features and considering the importance of features at different scales. This paper proposes a novel, end-to-end, fully convolutional network to integrate a multiconnection ResNet model and a class-specific attention model into a unified framework to overcome these problems. The former fuses multilevel deep features without introducing any redundant information from low-level features. The latter can learn the contributions from different features of each geo-object at each scale. Extensive experiments on two open datasets indicate that the proposed method can achieve class-specific scale-adaptive classification results and it outperforms other state-of-the-art methods. The results were submitted to the International Society for Photogrammetry and Remote Sensing (ISPRS) online contest for comparison with more than 50 other methods. The results indicate that the proposed method (ID: SWJ_2) ranks #1 in terms of overall accuracy, even though no additional digital surface model (DSM) data that were offered by ISPRS were used and no postprocessing was applied.
EISSN: 2072-4292
DOI: 10.3390/rs11131617
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
The following publication Wang, J.; Shen, L.; Qiao, W.; Dai, Y.; Li, Z. Deep Feature Fusion with Integration of Residual Connection and Attention Model for Classification of VHR Remote Sensing Images. Remote Sens. 2019, 11, 1617, 1-22 is available at
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