18 October 2016 Contextual descriptors and neural networks for scene analysis in VHR SAR images
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Abstract
The development of SAR technology during the last decade has made it possible to collect a huge amount of data over many regions of the world. In particular, the availability of SAR images from different sensors, with metric or sub-metric spatial resolution, offers novel opportunities in different fields as land cover, urban monitoring, soil consumption etc. On the other hand, automatic approaches become crucial for the exploitation of such a huge amount of information. In such a scenario, especially if single polarization images are considered, the main issue is to select appropriate contextual descriptors, since the backscattering coefficient of a single pixel may not be sufficient to classify an object on the scene. In this paper a comparison among three different approaches for contextual features definition is presented so as to design optimum procedures for VHR SAR scene understanding. The first approach is based on Gray Level Co- Occurrence Matrix since it is widely accepted and several studies have used it for land cover classification with SAR data. The second approach is based on the Fourier spectra and it has been already proposed with positive results for this kind of problems, the third one is based on Auto-associative Neural Networks which have been already proven effective for features extraction from polarimetric SAR images. The three methods are evaluated in terms of the accuracy of the classified scene when the features extracted using each method are considered as input to a neural network classificator and applied on different Cosmo-SkyMed spotlight products.
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Fabio Del Frate, Fabio Del Frate, Matteo Picchiani, Matteo Picchiani, Alessia Falasco, Alessia Falasco, Giovanni Schiavon, Giovanni Schiavon, "Contextual descriptors and neural networks for scene analysis in VHR SAR images", Proc. SPIE 10003, SAR Image Analysis, Modeling, and Techniques XVI, 1000304 (18 October 2016); doi: 10.1117/12.2241759; https://doi.org/10.1117/12.2241759
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