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Landcover classification using the fusion of HSI and LiDAR data.

Python 100.00%

classification convolutional-neural-networks deep-learning fully-connected-network machine-learning python fusion hyperspectral-image-classification lidar lidar-point-cloud

lidar-and-hyperspectral-fusion-classification's Introduction

LiDAR-and-Hyperspectral-Fusion-classification

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

Steps to Run:

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

Results:

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

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.

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