Salient Object Detection Based on Progressively Supervised Learning for Remote Sensing Images

2021 ◽  
pp. 1-15
Author(s):  
Libao Zhang ◽  
Jie Ma
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Supervised Learning ◽  
Salient Object Detection ◽  
Salient Object ◽  
Remote Sensing Images

scholarly journals Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images

2021 ◽  
Vol 30 ◽  
pp. 1305-1317
Author(s):  
Qijian Zhang ◽  
Runmin Cong ◽  
Chongyi Li ◽  
Ming-Ming Cheng ◽  
Yuming Fang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Salient Object Detection ◽  
Optical Remote Sensing ◽  
Salient Object ◽  
Remote Sensing Images ◽  
Fluid Network

scholarly journals A parallel down-up fusion network for salient object detection in optical remote sensing images

2020 ◽  
Vol 415 ◽  
pp. 411-420 ◽  
Author(s):  
Chongyi Li ◽  
Runmin Cong ◽  
Chunle Guo ◽  
Hua Li ◽  
Chunjie Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Salient Object Detection ◽  
Optical Remote Sensing ◽  
Salient Object ◽  
Remote Sensing Images

scholarly journals Semantic-Guided Attention Refinement Network for Salient Object Detection in Optical Remote Sensing Images

2021 ◽  
Vol 13 (11) ◽  
pp. 2163
Author(s):  
Zhou Huang ◽  
Huaixin Chen ◽  
Biyuan Liu ◽  
Zhixi Wang
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Network Architecture ◽  
Salient Object Detection ◽  
Optical Remote Sensing ◽  
Salient Object ◽  
Remote Sensing Images ◽  
Multi Level ◽  
Object Area ◽  
High Level

Although remarkable progress has been made in salient object detection (SOD) in natural scene images (NSI), the SOD of optical remote sensing images (RSI) still faces significant challenges due to various spatial resolutions, cluttered backgrounds, and complex imaging conditions, mainly for two reasons: (1) accurate location of salient objects; and (2) subtle boundaries of salient objects. This paper explores the inherent properties of multi-level features to develop a novel semantic-guided attention refinement network (SARNet) for SOD of NSI. Specifically, the proposed semantic guided decoder (SGD) roughly but accurately locates the multi-scale object by aggregating multiple high-level features, and then this global semantic information guides the integration of subsequent features in a step-by-step feedback manner to make full use of deep multi-level features. Simultaneously, the proposed parallel attention fusion (PAF) module combines cross-level features and semantic-guided information to refine the object’s boundary and highlight the entire object area gradually. Finally, the proposed network architecture is trained through an end-to-end fully supervised model. Quantitative and qualitative evaluations on two public RSI datasets and additional NSI datasets across five metrics show that our SARNet is superior to 14 state-of-the-art (SOTA) methods without any post-processing.

Salient Object Detection Based on Deep Residual Networks and Edge Supervised Learning

2019 ◽  
Vol 56 (15) ◽  
pp. 151502
Author(s):  
时斐斐 Feifei Shi ◽  
张松龙 Songlong Zhang ◽  
彭力 Li Peng
Keyword(s):  
Object Detection ◽  
Supervised Learning ◽  
Salient Object Detection ◽  
Salient Object
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