/LiDAR-and-Hyperspectral-Fusion-classification

Landcover classification using the fusion of HSI and LiDAR data.

Primary LanguagePython

LiDAR-and-Hyperspectral-Fusion-classification

Landcover classifcation using the fusion of hyperspectral data and LiDAR neighbourhood region of NxN surrounding a pixel.

fusion

Steps to Run:

  1. Create a virtual environment using command: virtualenv myenv
  2. Activate the virtual environment: source venv/bin/activate
  3. Install the requirements file: pip install -r requirements.txt
  4. Download this gulfport mat file in the same directory.
  5. Run the file: python main.py

Results:

An accuracy of 93.17% ± 1.20 was achieved. The Confusion matrix is shown below: hsi+lidar_github

References

P. Gader, A. Zare, R. Close, J. Aitken, G. Tuell, “MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set,” University of Florida, Gainesville, FL, Tech. Rep. REP-2013-570, Oct. 2013.

X. Du and A. Zare, “Technical Report: Scene Label Ground Truth Map for MUUFL Gulfport Data Set,” University of Florida, Gainesville, FL, Tech. Rep. 20170417, Apr. 2017.