A Parallel Down-Up Fusion Network for Salient Object Detection in
Optical Remote Sensing Images
- URL: http://arxiv.org/abs/2010.00793v1
- Date: Fri, 2 Oct 2020 05:27:57 GMT
- Title: A Parallel Down-Up Fusion Network for Salient Object Detection in
Optical Remote Sensing Images
- Authors: Chongyi Li, Runmin Cong, Chunle Guo, Hua Li, Chunjie Zhang, Feng
Zheng, and Yao Zhao
- Abstract summary: We propose a novel Parallel Down-up Fusion network (PDF-Net) for salient object detection in optical remote sensing images (RSIs)
It takes full advantage of the in-path low- and high-level features and cross-path multi-resolution features to distinguish diversely scaled salient objects and suppress the cluttered backgrounds.
Experiments on the ORSSD dataset demonstrate that the proposed network is superior to the state-of-the-art approaches both qualitatively and quantitatively.
- Score: 82.87122287748791
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The diverse spatial resolutions, various object types, scales and
orientations, and cluttered backgrounds in optical remote sensing images (RSIs)
challenge the current salient object detection (SOD) approaches. It is commonly
unsatisfactory to directly employ the SOD approaches designed for nature scene
images (NSIs) to RSIs. In this paper, we propose a novel Parallel Down-up
Fusion network (PDF-Net) for SOD in optical RSIs, which takes full advantage of
the in-path low- and high-level features and cross-path multi-resolution
features to distinguish diversely scaled salient objects and suppress the
cluttered backgrounds. To be specific, keeping a key observation that the
salient objects still are salient no matter the resolutions of images are in
mind, the PDF-Net takes successive down-sampling to form five parallel paths
and perceive scaled salient objects that are commonly existed in optical RSIs.
Meanwhile, we adopt the dense connections to take advantage of both low- and
high-level information in the same path and build up the relations of cross
paths, which explicitly yield strong feature representations. At last, we fuse
the multiple-resolution features in parallel paths to combine the benefits of
the features with different resolutions, i.e., the high-resolution feature
consisting of complete structure and clear details while the low-resolution
features highlighting the scaled salient objects. Extensive experiments on the
ORSSD dataset demonstrate that the proposed network is superior to the
state-of-the-art approaches both qualitatively and quantitatively.
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