GitHub / rohitgandikota / Land-Use-Land-Cover-Classification-of-Satellite-Images-using-Deep-Learning
This work discusses how high resolution satellite images are classified into various classes like cloud, vegetation, water and miscellaneous, using feed forward neural network. Open source python libraries like GDAL and keras were used in this work. This work is generic and can be used for satellite images of any resolution, but with MX band sensors.
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PURL: pkg:github/rohitgandikota/Land-Use-Land-Cover-Classification-of-Satellite-Images-using-Deep-Learning
Stars: 2
Forks: 3
Open issues: 0
License: mit
Language: Python
Size: 11.7 KB
Dependencies parsed at: Pending
Created at: over 5 years ago
Updated at: over 2 years ago
Pushed at: over 5 years ago
Last synced at: over 1 year ago
Commit Stats
Commits: 5
Authors: 1
Mean commits per author: 5.0
Development Distribution Score: 0.0
More commit stats: https://commits.ecosyste.ms/hosts/GitHub/repositories/rohitgandikota/Land-Use-Land-Cover-Classification-of-Satellite-Images-using-Deep-Learning
Topics: deep-learning, gan, lulc-classification, remote-sensing, satellite-imagery