SPATIAL RESLUTION SENSITIVITY ANALYSIS OF CLASSIFCIATION OF SENTINEL-2 IMAGES BY PRE-TRAINED DEEP MODELS FROM BIG EARTH NET DATABASE
Keywords: Deep Learning, Convolutional Neural Network, Pre-Training, Big Earth Net, Residual Network
Abstract. Classifying and monitoring different vegetation types is important for forest management, food resources, and assessing the potential impacts of climate change. In this regard, several methods have been developed to study them using remote sensing data, and with the advent of neural networks, new methods are being proposed, especially in the field of automatic land use classification. In this research, multispectral Sentinel-2 satellite image has been used due to having spectral information and different spatial resolution for classifying plant species. Deep learning models have the ability to learn and recognize different features of images, but require a large number of training samples, so we used pre-trained ResNet networks with depths of 50, 101 and 152 layers, that trained with BigEarthNet dataset. The main purpose of this study is to evaluate the sensitivity of ResNet networks to spatial resolution. Results show that ResNet 101 was more stable than other networks, and the Resent 50 with an overall accuracy of 76.2 has the highest accuracy at a resolution of 20 meters.