Chrome Extension
WeChat Mini Program
Use on ChatGLM

MDAFNet: Monocular Depth-Assisted Fusion Networks for Semantic Segmentation of Complex Urban Remote Sensing Data

International Geoscience and Remote Sensing Symposium (IGARSS)(2023)

Chinese Univ Hong Kong | MizarVision

Cited 0|Views4
Abstract
This work proposes an end-to-end Monocular Depth-Assisted Fusion Network (MDAFNet) for semantic segmentation of complex urban remote sensing data. The proposed MDAFNet consists of a Monocular Depth Estimation Network (MDENet) and a Crossmodal Fusion Network (CFNet). More specifically, the MDENet first generates the earth surface depth information while the CFNet fuses the generated depth information and RGB images to address the segmentation task. In particular, the MDENet is capable of effectively extracting features of the ground surface while overcoming artifacts such as building shadows. Furthermore, the CFNet is designed to perform segmentation by extracting and fusing semantic information from generated depth information and Red-Green-Blue (RGB) images. Extensive experiments performed on a large-scale fine-resolution remote sensing dataset named the ISPRS Vaihingen confirm that the proposed MDAFNet outperforms conventional crossmodal models equipped with Digital Surface Model information.
More
Translated text
Key words
CFNet,complex urban remote sensing data,Crossmodal Fusion Network,Digital Surface Model information,earth surface depth information,end-to-end Monocular Depth-Assisted Fusion Network,generated depth information,large-scale fine-resolution remote sensing dataset,MDAFNet,MDENet,Monocular Depth Estimation Network,Monocular Depth-Assisted Fusion networks,Red-Green-Blue images,segmentation task,semantic information,semantic segmentation
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest