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

Abstract

Arising from the various object types and scales, diverse imaging orientations, and cluttered backgrounds in optical remote sensing image (RSI), it is difficult to directly extend the success of salient object detection for nature scene image to the optical RSI. In this paper, we propose an end-to-end deep network called LV-Net based on the shape of network architecture, which detects salient objects from optical RSIs in a purely data-driven fashion. The proposed LV-Net consists of two key modules, i.e., a two-stream pyramid module (L-shaped module) and an encoder-decoder module with nested connections (V-shaped module). Specifically, the L-shaped module extracts a set of complementary information hierarchically by using a two-stream pyramid structure, which is beneficial to perceiving the diverse scales and local details of salient objects. The V-shaped module gradually integrates encoder detail features with decoder semantic features through nested connections, which aims at suppressing the cluttered backgrounds and highlighting the salient objects. In addition, we construct the first publicly available optical RSI dataset for salient object detection, including 800 images with varying spatial resolutions, diverse saliency types, and pixel-wise ground truth. Experiments on this benchmark dataset demonstrate that the proposed method outperforms the state-of-the-art salient object detection methods both qualitatively and quantitatively.

11 pages, 8 figures, has been accepted by TGRS

References in corpus (5)

• Remote Sensing Image Scene Classification: Benchmark and State of the Art
• AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
• A Survey on Object Detection in Optical Remote Sensing Images
• Salient Object Detection: A Benchmark
• Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation

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• Re-thinking Co-Salient Object Detection
• Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation
• Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment
• Salient Object Detection in Optical Remote Sensing Images Driven by Transformer
• Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images
• A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images
• Salient Objects in Clutter
• Texture-Semantic Collaboration Network for ORSI Salient Object Detection
• Boundary-semantic collaborative guidance network with dual-stream feedback mechanism for salient object detection in optical remote sensing imagery
• UniUIR: Considering Underwater Image Restoration as An All-in-One Learner
• An End-to-End Network for Co-Saliency Detection in One Single Image
• Hyperspectral Remote Sensing Images Salient Object Detection: The First Benchmark Dataset and Baseline
• RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images
• Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images