Building detection in very high resolution multispectral data with deep learning features

Abstract

International audienceThe automated man-made object detection and building extraction from single satellite images is, still, one of the most challenging tasks for various urban planning and monitoring engineering applications. To this end, in this paper we propose an automated building detection framework from very high resolution remote sensing data based on deep convolu-tional neural networks. The core of the developed method is based on a supervised classification procedure employing a very large training dataset. An MRF model is then responsible for obtaining the optimal labels regarding the detection of scene buildings. The experimental results and the performed quantitative validation indicate the quite promising potentials of the developed approach

Benchmarking Deep Learning Frameworks for the Classification of Very High Resolution Satellite Multispectral Data

“…Method SAT-4 SAT-6 DBN [Basu et al, 2015] 81.78 76.41 CNN [Basu et al, 2015] 86.83 79.06 SDAE [Basu et al, 2015] 79.98 78.43 Semi-supervised [Basu et al, 2015] 97.95 93.92 Pretrained-AlexNet [Vakalopoulou et al, 2015] 99.46 99.57…”

“…Based on the DeepSat dataset, we have trained different models and reported on their performances. In particular, AlexNet, AlexNet-small and VGG models have been implemented and trained on the DeepSat dataset [Krizhevsky et al, 2012, Jaderberg et al, 2015, Ioffe and Szegedy, 2015, Vakalopoulou et al, 2015. The high accuracy rates demonstrate the potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing imagery.…”

“…Several deep architectures [Deng, 2014, Schmidhuber, 2015 have been employed, with the Deep Belief Networks, Autoencoders, Convolutional Neural Networks and Deep Boltzmann Machines being some of the most commonly used in the literature for a variety of problems. In particular, for the classification of remote sensing data certain deep architectures have provided highly accurate results [Mnih and Hinton, 2010, Chen et al, 2014, Vakalopoulou et al, 2015, Basu et al, 2015, Makantasis et al, 2015, Marmanis et al, 2016.…”

“…Similar to [Vakalopoulou et al, 2015, Marmanis et al, 2016] the already pretrained AlexNet network [Krizhevsky et al, 2012] has been employed, here, for feature extraction. In particular, features from the last layer (FC7) were extracted using two spectral band combinations (red-green-blue and NIR-red-green).…”

Tensor-Based Classification Models for Hyperspectral Data Analysis

“…The learning algorithm minimizes the negative log-likelihood (see relation (13)) with respect to network responses…”