Remote Sensing Scene Classification Based on Covariance Pooling of Multi-layer CNN Features Guided by Saliency Maps
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EN
Chapitre d'ouvrage
Este ítem está publicado en
Pattern Recognition and Artificial Intelligence. ICPRAI 2022. 2022-01-01p. 579-590
Springer International Publishing
Resumen en inglés
The new generation of remote sensing imaging sensors enables high spatial, spectral and temporal resolution images with high revisit frequencies. These sensors allow the acquisition of multi-spectral and multi-temporal ...Leer más >
The new generation of remote sensing imaging sensors enables high spatial, spectral and temporal resolution images with high revisit frequencies. These sensors allow the acquisition of multi-spectral and multi-temporal images. The availability of these data has raised the interest of the remote sensing community to develop novel machine learning strategies for supervised classification. This paper aims at introducing a novel supervised classification algorithm based on covariance pooling of multi-layer convolutional neural network (CNN) features. The basic idea consists in an ensemble learning approach based on covariance matrices estimation from CNN features. Then, after being projected on the log-Euclidean space, an SVM classifier is used to make a decision. In order to give more strength to relatively small objects of interest in the scene, we propose to incorporate the visual saliency map in the process. For that, inspired by the theory of robust statistics, a weighted covariance matrix estimator is considered. Larger weights are given to more salient regions. Finally, some experiments on remote sensing classification are conducted on the UC Merced land use dataset. The obtained results confirm the potential of the proposed approach in terms of classification scene accuracy. It demonstrates, besides the interest of exploiting second order statistics and adopting an ensemble learning approach, the benefit of incorporating visual saliency maps.< Leer menos
Palabras clave en inglés
Covariance pooling
Saliency map
Ensemble learning
Multi-layer CNN features
Centros de investigación