Dense Fusion Classmate Network for Land Cover Classification

2019/11/19 by Chao Tian, Cong Li, Tian, Chao +3
Computer Science · Earth and Planetary Sciences · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote Sensing and Land Use #Remote-Sensing Image Classification

paper · pdf · doi:10.48550/arxiv.1911.08169

openalex publication_date 2019/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Recently, FCNs based methods have made great progress in semantic segmentation. Different with ordinary scenes, satellite image owns specific characteristics, which elements always extend to large scope and no regular or clear boundaries. Therefore, effective mid-level structure information extremely missing, precise pixel-level classification becomes tough issues. In this paper, a Dense Fusion Classmate Network (DFCNet) is proposed to adopt in land cover classification.

Citations

• Multi-Scale Context Aggregation by Dilated Convolutions
• DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
• An Overview of Multi-Task Learning in Deep Neural Networks
• Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation
• Deep Residual Learning for Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding

Related

• Feature Pyramid Network for Multi-Class Land Segmentation
• Land Cover Semantic Segmentation Using ResUNet
• LULC Segmentation of RGB Satellite Image Using FCN-8
• Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture
• Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation Framework