Deep Neural Networks for automatic extraction of features in time series satellite images
arXiv:2008.08432
Abstract
Many earth observation programs such as Landsat, Sentinel, SPOT, and Pleiades produce huge volume of medium to high resolution multi-spectral images every day that can be organized in time series. In this work, we exploit both temporal and spatial information provided by these images to generate land cover maps. For this purpose, we combine a fully convolutional neural network with a convolutional long short-term memory. Implementation details of the proposed spatio-temporal neural network architecture are provided. Experimental results show that the temporal information provided by time series images allows increasing the accuracy of land cover classification, thus producing up-to-date maps that can help in identifying changes on earth.
References in corpus (4)
- BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding
- Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition
- Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification
- A Novel Multi-Attention Driven System For Multi-Label Remote Sensing Image Classification