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Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent

Atharva Sharma1, Xiuwen Liu1, Xiaojun Yang2

  • 1Department of Computer Science, Florida State University, Tallahassee, FL 32306-4530, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|June 23, 2018
PubMed
Summary

A new patch-based recurrent neural network (PB-RNN) improves land cover classification accuracy using multi-temporal remote sensing data. This method significantly outperforms existing techniques, enhancing environmental sustainability research.

Keywords:
Deep learningLSTMsLand cover classificationMulti-temporal remote sensing imageryPatch-based RNNsSpatial context

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Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Artificial Intelligence in Earth Observation

Background:

  • Accurate land cover information is crucial for environmental sustainability research.
  • Existing land cover datasets often suffer from unacceptable accuracy due to reliance on single-date, pixel-based analysis of remote sensing imagery.
  • Developing effective image classification protocols is a key challenge for improving accuracy.

Discussion:

  • The proposed patch-based recurrent neural network (PB-RNN) system integrates multi-spectral, multi-temporal, and spatial information from remote sensing data.
  • It addresses challenges posed by clouds and shadows in multi-temporal data series by considering pixel interdependence.
  • The system is specifically designed to leverage the unique characteristics of multi-temporal remote sensing data.

Key Insights:

  • The PB-RNN system achieved a significant improvement in classification accuracy (97.21%) compared to various baseline methods, including pixel-based and single-image neural networks.
  • A pixel-based single-image neural network achieved only 64.74% accuracy in the same study.
  • The study demonstrates the effectiveness of incorporating spatial and sequential dependencies in multi-temporal remote sensing data classification.

Outlook:

  • The developed PB-RNN method offers a pathway to producing significantly more accurate land cover datasets over large geographical areas.
  • This advancement can support more robust environmental monitoring and sustainability initiatives.
  • Further research could explore the scalability and adaptability of PB-RNN to diverse ecosystems and sensor types.