Towards better exploiting convolutional neural networks for remote sensing scene classification


Nogueira K, Penatti OAB & dos Santos JA (2017) Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, pp. 539-556.

We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets or CNNs) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors. In many applications, especially including remote sensing, it is not feasible to fully design and train a new ConvNet, as this usually requires a considerable amount of labeled data and demands high computational costs. Therefore, it is important to understand how to better use existing ConvNets. We perform experiments with six popular ConvNets using three remote sensing datasets. We also compare ConvNets in each strategy with existing descriptors and with state-of-the-art baselines. Results point that fine tuning tends to be the best performing strategy. In fact, using the features from the fine-tuned ConvNet with linear SVM obtains the best results. We also achieved state-of-the-art results for the three datasets used.

Signal Processing; Software; Artificial Intelligence; Computer Vision and Pattern Recognition

Pattern Recognition: Volume 61

FundersConselho Nacional de Desenvolvimento Científico e Tecnológico and CAPES, and Fapemig
Publication date31/01/2017
Publication date online02/07/2016
Date accepted by journal01/07/2016
PublisherElsevier BV

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