Principal Component Analysis (PCA) on LANDSAT-8 imagery

Applying PCA on the composite LANDSAT-8 satellite imagery.
96 download
Aggiornato 10 mar 2021

Visualizza la licenza

Step's that we have followed;

1. Create a composite of bands. In our case, we have created a
composite of 11 bands of LANDSAT-8 images (Dated: 26-12-2020).

2. Convert each band into a column vector.
We will get an array of size n x p. Where p=11 in our case.

3. Standardise the data and apply PCA.

4. Reconstruct the original data.

Cita come

ABHILASH SINGH (2024). Principal Component Analysis (PCA) on LANDSAT-8 imagery (https://www.mathworks.com/matlabcentral/fileexchange/88582-principal-component-analysis-pca-on-landsat-8-imagery), MATLAB Central File Exchange. Recuperato .

Compatibilità della release di MATLAB
Creato con R2020b
Compatibile con qualsiasi release
Compatibilità della piattaforma
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

PCA on LANDSAT8 imagery

Versione Pubblicato Note della release
1.0.0