Large scale land cover mapping from very high resolution (VHR) satellite images with convolutional neural network
Séminaires Planétologie et Sciences Spatiales
522, bât. Lamarck
Land cover mapping is the task of assigning each pixel of an overhead image a label, given a set of classes. This can be done manually, but coming at high costs, especially if one considers large geographic regions. Existing land cover maps are not satisfactory due to (i) a poor updateness rate (ii) a low spatial resolution (preventing from monitoring urban evolution) (iii) an inappropriate set of classes. Considering an automatic generation of such maps would allow annual updates and task-specific classes; specifically, SPOT 6&7 satellites provide us with a full France coverage at a resolution of 1.5m, in four spectral bands (RGB-IR) every year. This work investigates the potential of recent machine learning methods, namely the deep convolutional neural networks, in order to classify the aforementioned images.