Spaceborne GNSS-R Wind Speed Retrieval Using Machine Learning Methods

Machine learning methods for multimedia information retrieval

In this thesis we examined several multimodal feature extraction and learning methods for retrieval and classification purposes. We reread briefly some theoretical results of learning in Section 2 and reviewed several generative and discriminative models in Section 3 while we described the similarity kernel in Section 4. We examined different aspects of the multimodal image retrieval and classi...

Advanced machine learning methods for wind erosion monitoring in southern Iran

Extended abstract Introduction Wind erosion is one the most important factors of land degradation in the arid and semi-arid areas and it is one the most serious environmental problems in the world. In Fars province, 17 cities are prone to wind erosion and are considered as critical zones of wind erosion. One of the most important factors in soil wind erosion is land use/cover change. T...

Wind Direction Signatures in GNSS-R Observables from Space

Wind speed and direction are important essential climate variables (ECVs). GNSS-R is an emerging remote sensing technique that can be potentially used to retrieve wind speed from space. However, few studies have addressed the wind direction retrieval from spaceborne GNSS-R observables, namely the Delay Doppler map (DDM). In this study, the feasibility of retrieving wind direction from the synth...

Spaceborne Receiver Design for Scatterometric GNSS Reflectometry

Using Machine Learning for Image Retrieval

Current image retrieval techniques, while extremely accurate and effective on a relatively small scale, are unable to scale to very large database conditions due to the limitations imposed by the curse of dimensionality on nearest neighbor searching. In addition to this, the better systems are unsuited to visual browsing of the database. We propose the HUGINN framework for a concept-based image...