Mask-R-FCN: A Deep Fusion Network for Semantic Segmentation

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Zhang, Yunfeng, Chi, Mingmin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agricultural management; Algorithms; Artificial neural networks; Convolution; Datasets; Deep fusion; Deep semantic segmentation; Feature extraction; Fully convolutional network; Image classification; Image segmentation; Machine learning; Neural networks; Object detection; Object recognition; Remote sensing; Semantic segmentation; Semantics; Water resources management
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3012701

Remote sensing image classification plays a significant role in urban applications, precision agriculture, water resource management. The task of classification in the field of remote sensing is to map raw images to semantic maps. Typically, fully convolutional network (FCN) is one of the most effective deep neural networks for semantic segmentation. However, small objects in remote sensing images can be easily overlooked and misclassified as the majority label, which is often the background of the image. Although many works have attempted to deal with this problem, making a trade-off between background semantics and edge details is still a problem. This is mainly because they are based on a single neural network model. To deal with this problem, a convolutional deep network with regions (R-CNN), which is highly effective for object detection is leveraged as a complementary component in our work. A learning-based and decision-level strategy is applied to fuse both semantic maps from a semantic model and an object detection model. The proposed network is referred to as Mask-R-FCN. Experimental results on real remote sensing images from the Zurich dataset, Gaofen Image Dataset (GID), and DataFountain2017 show that the proposed network can obtain higher accuracy than single deep neural networks and other machine learning algorithms. The proposed network achieved better average accuracies, which are approximately 2% higher than those of any other single deep neural networks on the Zurich, GID, and DataFoundation2017 datasets.