Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks
This work addresses urban remote sensing for applications like mapping and monitoring, but it is incremental as it builds on existing deep learning methods with multimodal data.
The paper tackles semantic labeling of very high resolution multimodal remote sensing data by adapting deep fully convolutional networks to handle multimodal and multi-scale inputs, achieving state-of-the-art results on two public datasets.
In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data. Especially, we study how deep fully convolutional networks can be adapted to deal with multi-modal and multi-scale remote sensing data for semantic labeling. Our contributions are threefold: a) we present an efficient multi-scale approach to leverage both a large spatial context and the high resolution data, b) we investigate early and late fusion of Lidar and multispectral data, c) we validate our methods on two public datasets with state-of-the-art results. Our results indicate that late fusion make it possible to recover errors steaming from ambiguous data, while early fusion allows for better joint-feature learning but at the cost of higher sensitivity to missing data.