Real-time processing of images and videos is becoming considerably crucial in modern applications of machine learning (ML) and deep neural networks. Having a faster and compressed floating point arithmetic can significantly increase the performance of such applications optimizing memory occupation and transfer of information. In this field, the novel posit number system is very promising. In this paper we exploit posit numbers to evaluate the performance of several machine learning algorithms in real-time image and video processing applications. Future steps will involve further hardware accelerations for native posit operations.
This paper addresses bathymetry estimation from high resolution multispectral satellite images by proposing an accurate supervised method, based on a neuro-fuzzy approach. The method is applied to two Quickbird images of the same area, acquired in different years and meteorological conditions, and is validated using truth data. Performance is studied in different realistic situations of in situ data availability. The method allows to achieve a mean standard deviation of 36.7 cm for estimated water depths in the range [−18, −1] m. When only data collected along a closed path are used as a training set, a mean STD of 45 cm is obtained. The effect of both meteorological conditions and training set size reduction on the overall performance is also investigated.
This work describes the potential of oil spill classification from optical satellite images, as investigated by applying
different machine learning techniques to a dataset of more than 300 oil spill candidates, which have been detected from
multi-spectral satellite sensors during the years 2008 and 2009, over the entire area of the Mediterranean Sea. A set of
geometrical and grey level features from Synthetic Aperture Radar (SAR) literature has been extracted from the regions
of interest in order to characterize possible oil spills and feed the classification system. Results obtained by applying
different machine learning classifiers to the dataset, and the achieved performance are discussed. In particular, as a first
approach to oil spill classification, simple statistical classifiers and neural networks were used. Then, a more
interpretable fuzzy rule-based classifier was employed, and performance evaluation was refined by exploiting Receiver
Operating Characteristic (ROC) analysis. Finally, since oil spill dataset collection happens incrementally, a suitable
technique for online classification was proposed, encompassing at the same time cost-oriented classification, in order to
allow for a dynamic change of the misclassification costs. This latter goal has been achieved by building an ensemble of
cost-oriented, incremental and decremental support vector machines, exploiting the concept of the ROC convex hull.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.