Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery

Abstract

The trend towards higher resolution remote sensing imagery facilitates a transition from land-use classification to object-level scene understanding. Rather than relying purely on spectral content, appearance-based image features come into play. In this work, deep convolutional neural networks (CNNs) are applied to semantic labelling of high-resolution remote sensing data. Recent advances in fully convolutional networks (FCNs) are adapted to overhead data and shown to be as effective as in other domains. A full-resolution labelling is inferred using a deep FCN with no downsampling, obviating the need for deconvolution or interpolation. To make better use of image features, a pre-trained CNN is fine-tuned on remote sensing data in a hybrid network context, resulting in superior results compared to a network trained from scratch. The proposed approach is applied to the problem of labelling high-resolution aerial imagery, where fine boundary detail is important. The dense labelling yields state-of-the-art accuracy for the ISPRS Vaihingen and Potsdam benchmark data sets.

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• Dense Dilated Convolutions Merging Network for Land Cover Classification
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• Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images
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• Change Detection between Multimodal Remote Sensing Data Using Siamese CNN
• Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
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• Exploration of Convolutional Neural Network Architectures for Large Region Map Automation
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• Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach
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• MLPerf Mobile Inference Benchmark
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• Segmentation of VHR EO Images using Unsupervised Learning
• Analyzing Learned Convnet Features with Dirichlet Process Gaussian Mixture Models
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