This study investigates the potential of three advanced pixel window classification methods for habitat mapping, namely Kernel based spatial Re-Classification (KRC), Radial Basis Function (RBF) neural networks (NN) and Support Vector... more
This study investigates the potential of three advanced pixel window classification methods for habitat mapping, namely Kernel based spatial Re-Classification (KRC), Radial Basis Function (RBF) neural networks (NN) and Support Vector Machines (SVM). KRC classifier takes into account the spatial arrangement and frequency of spectral classes present within a predefined square kernel. On the other hand, RBF-NN and SVM classifiers
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Kernel-based reclassification algorithm derives information on specific thematic classes on the basis of the frequency and spatial arrangement of land cover classes within a square kernel. This algorithm has been originally developed and... more
Kernel-based reclassification algorithm derives information on specific thematic classes on the basis of the frequency and spatial arrangement of land cover classes within a square kernel. This algorithm has been originally developed and validated for the urban environment. The present work investigates the potential of projecting this technique to the classification of very high spatial resolution satellite imagery of natural
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AbstractThe monitoring and assessment of the status and trends of wetlands is of major concern for long-term biodiversity conservation initiatives. In particular, the coastal wetlands in the Mediterranean have undergone considerable land... more
AbstractThe monitoring and assessment of the status and trends of wetlands is of major concern for long-term biodiversity conservation initiatives. In particular, the coastal wetlands in the Mediterranean have undergone considerable land use and land cover changes in ...
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Littoral sea bottom properties can be mapped from Earth observation sensors if the reflection of the sea bottom contributes a detectable part to the signal measured by the sensor. This sea bottom reflection must be separated from all... more
Littoral sea bottom properties can be mapped from Earth observation sensors if the reflection of the sea bottom contributes a detectable part to the signal measured by the sensor. This sea bottom reflection must be separated from all other simultaneously measured portions of light for further mapping and classification procedures. Other contributors of light scattered to the sensor are atmospheric molecules and aerosols, the water surface reflection, and light scattered and absorbed due to particular properties of water constituents and the pure ...
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Littoral sea bottom properties can be mapped from Earth observation sensors if the reflection of the sea bottom contributes a detectable part to the signal measured by the sensor. This sea bottom reflection must be separated from all... more
Littoral sea bottom properties can be mapped from Earth observation sensors if the reflection of the sea bottom contributes a detectable part to the signal measured by the sensor. This sea bottom reflection must be separated from all other simultaneously measured portions of light for further mapping and classification procedures.