196,032 research outputs found
A neural network sea-ice cloud classification algorithm for copernicus sentinel-3 sea and land surface temperature radiometer
No full textA Neural Network approach to classify Sentinel-3 sea and land surface temperature radiometer (SLSTR) pixels over polar regions is presented. The proposed approach is based on a careful preliminary analysis aimed to simulate SLSTR observation by means of MODIS data. The latter have been considered because of the long available time series and the quality of cloud mask products. A large set of MODIS AQUA and TERRA products has been applied to develop the training set of the Neural Network classificator that has been tuned to discriminate clear ocean, clouds and sea-ice surfaces on the scene
Sentinel-2 change detection based on deep features
No full textIn this manuscript, we address the problem of change detection for Sentinel-2 data. The proposed method is based on deep features representation. First, multilevel convolutional neural network (CNN) features are extracted from input images acquired at different times. Then, euclidean distance is applied to generate dissimilarity map that indicate change probabilities of each pixel. Finally, bounding boxes corresponding the change areas can be obtained with clustering and an optimizing connected component labeling algorithm. Experiments on a manually annotated dataset demonstrate the feasibility and effectiveness of the proposed method
Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection
No full textIn this paper a new approach based on the fusion of Sentinel-1 and Sentinel-2 products to map urban change detection and to observe suburb's development is presented. The algorithm developed can process data in a fast, automatic and accurate way. To reach this goal, the processing chain uses an iterative multitemporal approach based, for each iteration, on three procedures. The first and second ones are based on Pulse Coupled Neural Network (PCNN) applied to SAR and optical images, respectively, while the third processing is an optical multiband filter, implementing the spectral difference computation. The three outputs of each iteration are fused together by means of a weighted average formulation. The algorithm may deal with multitemporal acquisitions to improve the overall accuracy in the detection of urban changes by the integration of the outputs at different time intervals
Use of neural networks and SAR interferometry for the automatic retrieval of tectonic parameters
No full textRetrieval of fault parameters of October 23, 2011 Eastern Turkey eartquake obtained by Neural Network
No full textWe have analysed the seismic source of the active fault generated Van Mw=7.1 earthquake occurred in Eastern Turkey the 23rd October 2011. To this aim the surface displacement field has been measured applying SAR Interferometry (InSAR) technique to the available dataset of coseismic COSMO-SkyMed image pairs. The seismic source model has been obtained by the use of a data inversion procedure based on the concurrent application of InSAR techniques and Neural Networks. The proposed approach elaborates the information on the coseismic deformation pattern stemming from available differential interferograms. The interferogram is the expression of the active fault at depth, thus its shape, size and its features somehow refer to the geometry and slip of the fault generating the seism. A Neural Network has been trained to recognize some fault parameters (Length, Width, Strike, Dip, Depth) from the unwrapped interferogram. The retrieval exercise consists in estimating these parameters from the coseismic interferogram exploiting Neural Networks
Contextual descriptors and neural networks for scene analysis in VHR SAR images
No full textThe 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
Compression of SAR interferograms for parameter retrieval using neural networks
No full textSAR interferograms are generally affected by different types of errors. Phase noise in interferometry is introduced by the radar system, by the propagation path through the variably refractive atmosphere, by spatial decorrelation of the electromagnetic fields scattered back from the surface elements. In many applicative cases, such as DEM generation, a pixel based information is required and noise can be reduced using a multilook technique which is often applied by averaging neighboring pixels. In other cases, the pixel based information is less important with respect to the fringes distribution pattern observed over the area of interest. More specifically, in applications regarding tectonics, the retrieval problem is often focused on the estimation of the fault parameters from the InSAR differential interferogram whereas this latter is generated by computing the phase difference of two radar images, acquired before and after an earthquake, on a pixel-by-pixel basis. Elements such as the shape and periodicity of the fringes, the number of lobes and their orientation represent the information contained in the interferogram. In such a case, besides the noise mitigation, it is also important to express the relevant information after having applied to the image some feature extraction technique, in order to avoid to design inversion algorithms receiving as input the value of each single pixel. The issue can be addressed by means of a spatial sampling, but this is not certainly an optimum solution for the problem. As far as we know, no specific techniques for dimensionality reduction applied to SAR interferograms have been presented in literature. In this paper two standard image filtering approaches based on harmonic analysis and a novel one based on autoassociative neural networks (AANN) are analysed. A specific application for the estimation of tectonic parameters from SAR interferometry is also presented
An automatic neural network approach for retrieving seismic source parameters from INSAR data
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