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

By Chongyi Li, Runmin Cong, Junhui Hou, Sanyi Zhang, Y. Qian, Sam Kwong. IEEE Transactions on Geoscience and Remote Sensing, 2019

307 citations recorded by the source corpus.

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 data set for salient object detection, including 800 images with varying spatial resolutions, diverse saliency types, and pixel-wise ground truth. Experiments on this benchmark data set demonstrate that the proposed method outperforms the state-of-the-art salient object detection methods both qualitatively and quantitatively.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology

View Paper: https://www.semanticscholar.org/paper/1235a7219fe114be5375c8fc7b12efaf00a3cb82

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Jing Zhang
3. Chongyi Li
4. Bo Du
5. Chunle Guo
6. Runmin Cong
7. Di Wang
8. Qiming Zhang

Citations in the corpus

• Advancing Plain Vision Transformer Toward Remote Sensing Foundation Model (2022, methodology): “[4], [5], [6], [7], [8], [9], [10] In the current remote sensing (RS) field, convolutional neural networks (CNNs) are the most commonly used models for extracting hierarchical multiscale visual features.”
• Saving 100x Storage: Prototype Replay for Reconstructing Training Sample Distribution in Class-Incremental Semantic Segmentation (2023, background): “Typically, neural networks [6, 15, 16] are trained in a one-time fashion.”
• PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators (2023, background): “In recent years, deep learning technology has also greatly promoted the development of the computer vision task, including object detection [26]–[33], medical analysis [34]–[37], remote sensing interpretation [38]–[41], content enhancement [13], [14], [42], [43], and underwater image enhancement [44”
• SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection (2022, background): “Recently, SOD in RGBT image[11], light field image[12], [13], [14], high-resolution image[15], [16], optical remote sensing image[17], [18], [19] and 360◦ omnidirectional image[20], [21] have been gradually researched.”