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.
Original language | English |
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Pages (from-to) | 411-420 |
Number of pages | 10 |
Journal | Neurocomputing |
Volume | 415 |
Early online date | 14 Sept 2020 |
DOIs | |
Publication status | Published - 20 Nov 2020 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2020 Elsevier B.V.
Funding
This work was supported in part by the Beijing Nova Program under Grant Z201100006820016, in part by the National Key Research and Development of China under Grant 2018AAA0102100, in part by the National Natural Science Foundation of China under Grant 62002014, Grant 61532005, Grant U1936212, Grant 61972188, and in part by China Postdoctoral Science Foundation under Grant 2020T130050, Grant 2019M660438. Chunjie Zhang was supported by the Fundamental Research Funds for the Central Universities under Grant 2019RC040.
Keywords
- Deep learning
- Optical remote sensing images
- Salient object detection