Sea-land segmentation is an important process for many key applications in remote sensing. Proper operative sea-land segmentation for remote sensing images remains a challenging issue due to complex and diverse transition between sea and land. Although several convolutional neural networks (CNNs) have been developed for sea-land segmentation, the performance of these CNNs is far from the expected target. This paper presents a novel deep neural network structure for pixel-wise sea-land segmentation, a residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images. RDU-Net is a combination of both downsampling and upsampling paths to achieve satisfactory results. In each downsampling and upsampling path, in addition to the convolution layers, several densely connected residual network blocks are proposed to systematically aggregate multiscale contextual information. Each dense network block contains multilevel convolution layers, short-range connections, and an identity mapping connection, which facilitates features reuse in the network and makes full use of the hierarchical features from the original images. These proposed blocks have a certain number of connections that are designed with shorter distance backpropagation between the layers and can significantly improve segmentation results while minimizing computational costs. We have performed extensive experiments on two real datasets, Google-Earth and ISPRS, and compared the proposed RDU-Net against several variations of dense networks. The experimental results show that RDU-Net outperforms the other state-of-the-art approaches on the sea-land segmentation tasks.
|Number of pages||14|
|Journal||IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing|
|Early online date||05 Aug 2019|
|Publication status||Published - 29 Sep 2019|
Bibliographical noteFunding Information:
Manuscript received January 20, 2019; revised May 6, 2019 and June 15, 2019; accepted June 17, 2019. Date of publication August 5, 2019; date of current version September 29, 2019. This work was supported in part by the National Science Foundation of China under Grants 61572315 and 6151101179, in part by the 973 Plan, China, under Grant 2015CB856004, and in part by the Marsden Fund of New Zealand. The work of H. Zhou was supported in part by the U.K. EPSRC under Grant EP/N011074/1, in part by the Royal Society-Newton Advanced Fellowship under Grant NA160342, and in part by the European Union’s Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie Grant Agreement 720325. (Corresponding author: Ruili Wang.) P. Shamsolmoali, M. Zareapoor, and J. Yang are with the Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China (e-mail: email@example.com; firstname.lastname@example.org; email@example.com).
This work was supported in part by the National Science Foundation of China under Grants 61572315 and 6151101179, in part by the 973 Plan, China, under Grant 2015CB856004, and in part by the Marsden Fund of New Zealand. The work of H. Zhou was supported in part by the U.K. EPSRC under Grant EP/N011074/1, in part by the Royal Society-Newton Advanced Fellowship under Grant NA160342, and in part by the European Union's Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie Grant Agreement 720325.
© 2019 IEEE.
Copyright 2019 Elsevier B.V., All rights reserved.
- Deep neural network (DNN)
- dense network (DenseNet)
- remote sensing images
- sea-land segmentation
ASJC Scopus subject areas
- Computers in Earth Sciences
- Atmospheric Science