Random Forest Supervised Classification Using Sentinel-2 Data
Introduction to Multi-spectral Imaging
Multispectral imaging (MSI) captures image data within specific wavelengths ranges across electromagnetic spectrum. MSI detects different images through instruments that are sensitive to different wavelengths of light thus allowing for distinction in land-type. MSI is a highly informative form of imaging technique as it can move beyond visible light range and can detect and extract data that the human eye fails to capture. Sentinel 2 is an Earth Observation mission from the Copernicus programme that acquires high resolutions of multispectral imagery by conducting frequent visits over a given area. Sentinel-2 is a polar-orbiting Earth Observation Mission from the Copernicus programme that conducts multispectral high-resolution imaging for land monitoring to provide, for example, imagery of vegetation, soil and water cover, inland waterways, and coastal areas. Sentinel-2 can also deliver information for emergency services. Sentinel-2A was launched on 23 June 2015 and Sentinel-2B followed on 7 March 2017. The Sentinel-2 has 13 bands of multispectral data in the visible, near infrared and short-wave infrared part of the spectrum. This tutorial applies images captured by the Sentinel 2A satellite, which provides similar functions to the ones mentioned above.
Supervised and Random Forest Classification
Supervised Classification is a technique used for extracting information from image data. The process includes classification of pixels of an image into different classes based on features of the pixels. Supervised classification can be conducted in 2 main stages. The first stage is called Training Stage and the latter is called Classification Stage. In the training stage of the process, a set of vectors called training sample is established by the user through which the supervised classification is conducted. The number of classifiers is depended upon the number of inputs by the user. For example, if the user identifies 5 different land cover classes in its training data, the supervised classification conducted will output an image of the scene using the data and will have 5 different class distinctions. This tutorial uses a specific type of supervised classification technique called Random Forests.
Random Forest Classification is a Supervised form of classification and regression. Random forest or random decision forests are a form of Model Ensembling techniques. Model Ensembling attempts to aggregate large number of test models to improve accuracy in classification and regression. Random Forest works on the principle of Model Ensembling and helps provide accurate low-cost classified images using a baseline of training data. Higher the training data for classification, higher the accuracy in the end product.
