Multi-Label Classification on Remote-Sensing Images

01/06/2022
by   Aditya Kumar Singh, et al.
10

Acquiring information on large areas on the earth's surface through satellite cameras allows us to see much more than we can see while standing on the ground. This assists us in detecting and monitoring the physical characteristics of an area like land-use patterns, atmospheric conditions, forest cover, and many unlisted aspects. The obtained images not only keep track of continuous natural phenomena but are also crucial in tackling the global challenge of severe deforestation. Among which Amazon basin accounts for the largest share every year. Proper data analysis would help limit detrimental effects on the ecosystem and biodiversity with a sustainable healthy atmosphere. This report aims to label the satellite image chips of the Amazon rainforest with atmospheric and various classes of land cover or land use through different machine learning and superior deep learning models. Evaluation is done based on the F2 metric, while for loss function, we have both sigmoid cross-entropy as well as softmax cross-entropy. Images are fed indirectly to the machine learning classifiers after only features are extracted using pre-trained ImageNet architectures. Whereas for deep learning models, ensembles of fine-tuned ImageNet pre-trained models are used via transfer learning. Our best score was achieved so far with the F2 metric is 0.927.

READ FULL TEXT

page 20

page 21

page 40

research
12/11/2019

Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models

Land cover mapping and monitoring are essential for understanding the en...
research
10/26/2022

RapidAI4EO: Mono- and Multi-temporal Deep Learning models for Updating the CORINE Land Cover Product

In the remote sensing community, Land Use Land Cover (LULC) classificati...
research
09/02/2018

Identifying Land Patterns from Satellite Imagery in Amazon Rainforest using Deep Learning

The Amazon rainforests have been suffering widespread damage, both via n...
research
03/06/2022

Prediction of paddy cultivation using deep learning on land cover variation for sustainable agriculture

Geospatial analytics is a promising method of spatial data processing an...
research
07/20/2021

Understanding Gender and Racial Disparities in Image Recognition Models

Large scale image classification models trained on top of popular datase...
research
01/31/2022

AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning

A key challenge of supervised learning is the availability of human-labe...
research
08/06/2021

Improving Global Forest Mapping by Semi-automatic Sample Labeling with Deep Learning on Google Earth Images

Global forest cover is critical to the provision of certain ecosystem se...

Please sign up or login with your details

Forgot password? Click here to reset