Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark
Authors:
- Kangkang LuKLKangkang Lu
- Nicolas AudeberrNANicolas Audeberr
- Sebastien LefevreSebastien Lefevre
- 1 July 2018
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 6947-6950
Abstract
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.Keywords
This publication has 7 references indexed in Scilit:
- DeepRoadMapper: Extracting Road Topology from Aerial Images2017
- High-Resolution Aerial Image Labeling With Convolutional Neural NetworksIEEE Transactions on Geoscience and Remote Sensing, 2017
- Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark2017
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification2015
- U-Net: Convolutional Networks for Biomedical Image Segmentation2015
- The signed Euclidean distance transform and its applications2003