EPFNet: Edge-Prototype Fusion Network Toward Few-Shot Semantic Segmentation for Aerial Remote-Sensing Images

Article Geochemistry & Geophysics

Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images

Gongyang Li et al.

Summary: In this article, a novel multi-content complementation network (MCCNet) is proposed for salient object detection in optical remote sensing images. Experimental results demonstrate that MCCNet outperforms other state-of-the-art methods in this field.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Scale-Aware Detailed Matching for Few-Shot Aerial Image Semantic Segmentation

Xiwen Yao et al.

Summary: This study proposes a scale-aware few-shot semantic segmentation network for aerial images. By performing detailed matching with multiple prototypes, the proposed method can accurately segment query images. In addition, a benchmark dataset is provided for research purposes.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Computer Science, Artificial Intelligence

Prior Guided Feature Enrichment Network for Few-Shot Segmentation

Zhuotao Tian et al.

Summary: This article proposes a Prior Guided Feature Enrichment Network (PFENet) to address the challenges of reduced generalization ability on unseen classes and spatial inconsistency between query and support targets in few-shot segmentation. PFENet includes a training-free prior mask generation method and a Feature Enrichment Module (FEM). Experimental results demonstrate that PFENet significantly improves the baseline method and achieves outstanding performance even without labeled support samples.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

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Article Environmental Sciences

Semi-Supervised Contrastive Learning for Few-Shot Segmentation of Remote Sensing Images

Yadang Chen et al.

Summary: This work presents a few-shot segmentation of remote sensing images with a self-supervised background learner to boost the generalization capacity for unseen categories to handle this challenge. The methodology is divided into two main modules: a meta learner and a background learner, expanding on the classic metric learning framework by optimizing feature representation.

REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Few-Shot Segmentation of Remote Sensing Images Using Deep Metric Learning

Xufeng Jiang et al.

Summary: This letter proposes a few-shot learning-based method for semantic segmentation of remote sensing images, which can perform semantic labeling for unseen object categories with limited annotated samples and has good generalization abilities.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

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Article Computer Science, Information Systems

Visible and thermal images fusion architecture for few-shot semantic segmentation

Yanqi Bao et al.

Summary: This research proposed a visible and thermal few-shot semantic segmentation task, established a corresponding dataset, and introduced a fusion architecture to enhance segmentation performance.

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION (2021)

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Article Automation & Control Systems