A lightweight multi-scale context network for salient object detection in optical remote sensing images

• URL: http://arxiv.org/abs/2205.08959v1
• Date: Wed, 18 May 2022 14:32:47 GMT
• Title: A lightweight multi-scale context network for salient object detection in optical remote sensing images
• Authors: Yuhan Lin, Han Sun, Ningzhong Liu, Yetong Bian, Jun Cen, Huiyu Zhou
• Abstract summary: We propose a multi-scale context network, namely MSCNet, for salient object detection in optical RSIs. Specifically, a multi-scale context extraction module is adopted to address the scale variation of salient objects. In order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism.
• Score: 16.933770557853077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for optical RSIs becomes a huge challenge. However, different from natural scene images (NSIs), the discussion on the optical RSI SOD task still remains scarce. In this paper, we propose a multi-scale context network, namely MSCNet, for SOD in optical RSIs. Specifically, a multi-scale context extraction module is adopted to address the scale variation of salient objects by effectively learning multi-scale contextual information. Meanwhile, in order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism for gradually aggregating and refining the salient regions from the multi-scale context extraction module. Extensive experiments on two benchmarks demonstrate that MSCNet achieves competitive performance with only 3.26M parameters. The code will be available at https://github.com/NuaaYH/MSCNet.

Related papers

• Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering [26.8129265632403]
  Current Remote Sensing Visual Question Answering (RSVQA) methods are limited by the imaging mechanisms of optical sensors. We propose a Text-guided Coarse-to-Fine Fusion Network (TGFNet) to improve RSVQA performance. We create the first large-scale benchmark dataset for evaluating optical-SAR RSVQA methods.
  arXiv  Detail & Related papers  (2024-11-24T09:48:03Z)
• Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment [61.45639694373033]
  We propose a novel lightweight network for optical remote sensing images (ORSI-SOD) based on semantic matching and edge alignment, termed SeaNet. Specifically, SeaNet includes a lightweight MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM) for high-level features, and a portable decoder for inference.
  arXiv  Detail & Related papers  (2023-01-07T04:33:51Z)
• Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images [102.75699068451166]
  We propose a novel Adjacent Context Coordination Network (ACCoNet) to explore the coordination of adjacent features in an encoder-decoder architecture for optical RSI-SOD. The proposed ACCoNet outperforms 22 state-of-the-art methods under nine evaluation metrics, and runs up to 81 fps on a single NVIDIA Titan X GPU.
  arXiv  Detail & Related papers  (2022-03-25T14:14:55Z)
• Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images [108.79667788962425]
  salient object detection in optical remote sensing images (RSI-SOD) remains to be a challenging emerging topic. We propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD. In MCCM, we consider multiple types of features that are critical to RSI-SOD, including foreground features, edge features, background features, and global image-level features.
  arXiv  Detail & Related papers  (2021-12-02T04:46:40Z)
• RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images [82.1679766706423]
  Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. We propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs. Our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2021-10-27T07:18:32Z)
• CMA-Net: A Cascaded Mutual Attention Network for Light Field Salient Object Detection [17.943924748737622]
  We propose CMA-Net, which consists of two novel cascaded mutual attention modules aiming at fusing the high level features from the modalities of all-in-focus and depth. Our proposed CMA-Net outperforms 30 SOD methods (by a large margin) on two widely applied light field benchmark datasets.
  arXiv  Detail & Related papers  (2021-05-03T15:32:12Z)
• Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images [193.77450545067967]
  We propose an end-to-end Dense Attention Fluid Network (DAFNet) for salient object detection in optical remote sensing images (RSIs) A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships. We construct a new and challenging optical RSI dataset for SOD that contains 2,000 images with pixel-wise saliency annotations.
  arXiv  Detail & Related papers  (2020-11-26T06:14:10Z)
• A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images [82.87122287748791]
  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.
  arXiv  Detail & Related papers  (2020-10-02T05:27:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.