687 citations · 1.1k across the 33 of their papers we have counts for
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How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?
Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework. Especially, we…
Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks
Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
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…
On the usability of deep networks for object-based image analysis
Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
As computer vision before, remote sensing has been radically changed by the introduction of Convolution Neural Networks. Land cover use, object detection and scene understanding in…
Combining multiple resolutions into hierarchical representations for kernel-based image classification
Yanwei Cui, Sébastien Lefevre, Laetitia Chapel +1
Geographic object-based image analysis (GEOBIA) framework has gained increasing interest recently. Following this popular paradigm, we propose a novel multiscale classification app…
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach
Yanwei Cui, Laetitia Chapel, Sébastien Lefèvre
Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hier…
A Subpath Kernel for Learning Hierarchical Image Representations
Yanwei Cui, Laetitia Chapel, Sébastien Lefèvre
Tree kernels have demonstrated their ability to deal with hierarchical data, as the intrinsic tree structure often plays a discriminative role. While such kernels have been success…