Mapping Invasive Aquatic Plants in Sentinel-2 Images Using Convolutional Neural Networks Trained With Spectral Indices

Abstract

Multispectral images collected by the European Space Agency (ESA)'s Sentinel-2 satellite offer a powerful resource for accurately and efficiently mapping areas affected by the distribution of invasive aquatic plants. In this work, we use different spectral indices to detect invasive aquatic plants in the Guadiana river, Spain. Our methodology uses a convolutional neural network (CNN) as the baseline classifier and trains it using spectral indices calculated using different Sentinel-2 band combinations. Specifically, we consider the following spectral indices: with two bands, we calculate the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference infrared index (NDII). With three bands, we calculate the red-green-blue (RGB) composite and the floating algae index (FAI). Finally, we also use four bands to calculate the bare soil index (BSI). In our results, we observed that CNNs can better map invasive aquatic plants in the considered case study when trained intelligently (using spectral indices) as compared to using all spectral bands provided by the Sentinel-2 instrument.