Nested Network With Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images

Chongyi LI, Runmin CONG, Junhui HOU, Sanyi ZHANG, Yue QIAN, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

174 Citations (Scopus)


Arising from the various object types and scales,diverse imaging orientations, and cluttered backgrounds inoptical remote sensing image (RSI), it is difficult to directlyextend the success of salient object detection for nature sceneimage to the optical RSI. In this paper, we propose an end-toend deep network called LV-Net based on the shape of networkarchitecture, which detects salient objects from optical RSIs ina purely data-driven fashion. The proposed LV-Net consists oftwo key modules, i.e., a two-stream pyramid module (L-shapedmodule) and an encoder–decoder module with nested connections(V-shaped module). Specifically, the L-shaped module extractsa set of complementary information hierarchically by using atwo-stream pyramid structure, which is beneficial to perceivingthe diverse scales and local details of salient objects. TheV-shaped module gradually integrates encoder detail featureswith decoder semantic features through nested connections,which aims at suppressing the cluttered backgrounds and highlighting the salient objects. In addition, we construct the firstpublicly available optical RSI data set for salient object detection,including 800 images with varying spatial resolutions, diversesaliency types, and pixel-wise ground truth. Experiments onthis benchmark data set demonstrate that the proposed methodoutperforms the state-of-the-art salient object detection methodsboth qualitatively and quantitatively.
Original languageEnglish
Pages (from-to)9156-9166
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number11
Early online date9 Aug 2019
Publication statusPublished - Nov 2019
Externally publishedYes

Bibliographical note

This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2019RC039, in part by the National Natural Science Foundation of China under Grant 61871342, Grant 61803103, and Grant 61672443, in part by Hong Kong RGC General Research Funds under Grant 9042038 (CityU 11205314) and Grant 9042322 (CityU 11200116), and in part by Hong Kong RGC Early Career Schemes under Grant 9048123.


  • Nested connections
  • optical remote sensing images (RSIs)
  • salient object detection
  • two-stream pyramid module


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