Multi-label Cloud Segmentation Using a Deep Network

03/15/2019
by   Soumyabrata Dev, et al.
8

Different empirical models have been developed for cloud detection. There is a growing interest in using the ground-based sky/cloud images for this purpose. Several methods exist that perform binary segmentation of clouds. In this paper, we propose to use a deep learning architecture (U-Net) to perform multi-label sky/cloud image segmentation. The proposed approach outperforms recent literature by a large margin.

READ FULL TEXT
research
04/16/2019

CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation

We analyze clouds in the earth's atmosphere using ground-based sky camer...
research
06/09/2018

Autoencoders for Multi-Label Prostate MR Segmentation

Organ image segmentation can be improved by implementing prior knowledge...
research
01/03/2018

Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation

Glaucoma is a chronic eye disease that leads to irreversible vision loss...
research
03/02/2021

PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics

Background. Eosinophilic esophagitis (EoE) is an allergic inflammatory c...
research
12/17/2014

Deep Learning for Multi-label Classification

In multi-label classification, the main focus has been to develop ways o...
research
01/17/2017

Systematic study of color spaces and components for the segmentation of sky/cloud images

Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a cost-e...
research
10/10/2019

Multi-label Categorization of Accounts of Sexism using a Neural Framework

Sexism, an injustice that subjects women and girls to enormous suffering...

Please sign up or login with your details

Forgot password? Click here to reset