Multispectral Photogrammetry

This project was used for remote sensing classification from a multispectral camera. The camera Parrot Sequoia was first calibrated and tested on sample areas. Then, it was fixed to a civil drone and we flew over high altitudes forest to estimate the evolution of vegetation species. Colorply was used to create a multispectral cloud of points, to improve our classification by adding extra features. The clusters were made from a random forest skeleton, using all radiometries (RED, REG, NIR, GRE) and the points 3D positions.

In this repository, you can run the classification on the provided data [here](test/data/result/RVB_GRE_RED_REG_NIR_NDVI.ply). For this short example (for fast processing), the classification results are described as follows :

The confusion matrix for this sample is :

	Terrain	Oak	Shrub	Grass
Terrain	410	0	0	16
Oak	0	260	10	0
Shrub	0	10	137	16
Grass	23	0	11	192

Global accuracy : 92.07%.