Towards better exploiting convolutional neural networks for remote sensing scene classification
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 …
convolutional neural networks (ConvNets or CNNs) in different scenarios from the ones they …
Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?
OAB Penatti, K Nogueira… - Proceedings of the IEEE …, 2015 - cv-foundation.org
In this paper, we evaluate the generalization power of deep features (ConvNets) in two new
scenarios: aerial and remote sensing image classification. We evaluate experimentally …
scenarios: aerial and remote sensing image classification. We evaluate experimentally …
Rotation-based support vector machine ensemble in classification of hyperspectral data with limited training samples
J Xia, J Chanussot, P Du, X He - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
With different principles, support vector machines (SVMs) and multiple classifier systems
(MCSs) have shown excellent performances for classifying hyperspectral remote sensing …
(MCSs) have shown excellent performances for classifying hyperspectral remote sensing …
Improving spatial feature representation from aerial scenes by using convolutional networks
K Nogueira, WO Miranda… - 2015 28th SIBGRAPI …, 2015 - ieeexplore.ieee.org
The performance of image classification is highly dependent on the quality of extracted
features. Concerning high resolution remote image images, encoding the spatial features in …
features. Concerning high resolution remote image images, encoding the spatial features in …
A novel multi-classifier information fusion based on Dempster–Shafer theory: application to vibration-based fault detection
V Yaghoubi, L Cheng… - Structural Health …, 2022 - journals.sagepub.com
Achieving a high prediction rate is a crucial task in fault detection. Although various
classification procedures are available, none of them can give high accuracy in all …
classification procedures are available, none of them can give high accuracy in all …
CNN-DST: Ensemble deep learning based on Dempster–Shafer theory for vibration-based fault recognition
V Yaghoubi, L Cheng… - Structural Health …, 2022 - journals.sagepub.com
Nowadays, using vibration data in conjunction with pattern recognition methods is one of the
most common fault detection strategies for structures. However, their performances depend …
most common fault detection strategies for structures. However, their performances depend …
An ensemble classifier for vibration-based quality monitoring
Vibration-based quality monitoring of manufactured components often employs pattern
recognition methods. Albeit developing several classification methods, they usually provide …
recognition methods. Albeit developing several classification methods, they usually provide …
Change detection in SAR images based on matrix factorisation and a Bayes classifier
Change detection in synthetic aperture radar (SAR) images can be made as a matrix
factorisation model, and it can detect the changes based on the foreground information in …
factorisation model, and it can detect the changes based on the foreground information in …
Learning deep features on multiple scales for coffee crop recognition
R Baeta, K Nogueira, D Menotti… - 2017 30th SIBGRAPI …, 2017 - ieeexplore.ieee.org
Geographic mapping of coffee crops by using remote sensing images and supervised
classification has been a challenging research subject. Besides the intrinsic problems …
classification has been a challenging research subject. Besides the intrinsic problems …
Foreword to the special issue on machine learning for remote sensing data processing
The twenty-seven articles in this special issue is a follow-up to special sessions organized at
WHISPERS conferences. Such sessions drew unexpectedly large attendance, signaling the …
WHISPERS conferences. Such sessions drew unexpectedly large attendance, signaling the …