ASSESSMENT OF A MULTI-SENSOR APPROACH FOR NOISE REMOVAL ON LANDSAT-8 OLI TIME SERIES USING CBERS-4 MUX DATA TO IMPROVE CROP CLASSIFICATION BASED ON PHENOLOGICAL FEATURES

Abstract

In this work we investigated a method for noise removal on Landsat-8 OLI time series using CBERS-4 MUX data to improve crop classiï¬ cation. An algorithm was built to look to the nearest MUX image for each Landsat image, based on an user deï¬ ned time span. The algorithm checks for cloud contaminated pixels on the Landsat time series using Fmask and replaces the contaminated pixels to build the integrated time series (Landsat-8 OLI + CBERS-4 MUX). Phenological features were extracted from the time series samples for each method (EVI and NDVI original time series and multi sensor time series, with and without ï¬ ltering) and subjected to data mining using Random Forest classiï¬ cation. In general, we observed a slight increase in the classiï¬ cation accuracy when using the proposed method. The best result was observed with the EVI integrated ï¬ ltered time series (78%), followed by the ï¬ ltered Landsat EVI time series (76%).