Over the recent years, there has been an increasing interest in large-scale classification of remote sensing images. In this context, the Inria Aerial Image Labeling Benchmark has been released online in December 2016. In this paper, we discuss the outcomes of the first year of the benchmark contest, which consisted in dense labeling of aerial images into building/not building classes, covering areas of five cities not present in the training set. We present four methods with the highest numerical accuracies, all four being convolutional neural network approaches. It is remarkable that three of these methods use the U-net architecture, which has thus proven to become a new standard in image dense labeling.
|Title of host publication
|2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Oct 31 2018
|38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: Jul 22 2018 → Jul 27 2018
|International Geoscience and Remote Sensing Symposium (IGARSS)
|38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
|07/22/18 → 07/27/18
- Aerial images
- Classification benchmark
- Convolutional neural networks
- Deep learning