Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks

Abstract

This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation. Our contributions are the following: 1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; 2) we introduce a multi-kernel convolutional layer for fast aggregation of predictions at multiple scales; 3) we perform data fusion from heterogeneous sensors (optical and laser) using residual correction. Our framework improves state-of-the-art accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.

Asian Conference on Computer Vision (ACCV16), Nov 2016, Taipei, Taiwan

References in corpus (2)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Learning Deconvolution Network for Semantic Segmentation

Cited by in corpus (1)

• Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery