Unsupervised Learning
Unsupervised Learning

As you have seen in my previous work for classifying objects in satellite imagery, deep learning is used, and it does achieve good results, though it has a high dependency on the amount of available data, and more exactly labeled data.

Remote sensing creates a huge amount of data every day which mostly lacks annotations. The data complexity and the lack of a well-defined typology make it impossible to use supervised tools. And here is where clustering methods come in.

For better results:

Using all the bands available

For example, Sentinel 2 has up to 13 spectral bands available and this extra information can bring more clarity to the algorithm and eliminate noise.

Bellow, I will use all the 3 bands available for clustering and compare the results with the clustering obtained for using only 1 band.

Conclusions

Using all the bands available

In order to obtain the results above I have been using:

— images from both Planet and Sentinel

— Rasterio, Numpy, Scikit-learn, KMeans, Matplotlib, Python.

One can save the resulted images as a GeoTIFF

(either using rasterio or gdal) and it can be imported into a GIS platform for further study and classification.
['Daniel Moraite']
['Satellite Technology', 'Unsupervised Learning', 'Clustering', 'Satellite Imagery', 'Machine Learning']