1,721,010 research outputs found
Aspects of digital signal processing in radiometric remote sensing of geophysical variables
No full textOn the role of VIS radiation for the ozone information retrieval from SCIAMACHY data by means of neural network algorithms
No full textThe observation of ozone concentrations from satellite platform, on a systematic and continuous basis, is crucial to understand and monitor processes that can play a fundamental role for the life on Earth. UV/VIS spectra from dedicated sensors, as, e.g., SCIAMACHY, OMI or GOME-2, can provide useful height-resolved information about ozone. The most interesting spectral intervals for ozone remote sensing are the ultraviolet Hartley and Huggins bands and the visible Chappuis band. Optimal Estimation based inversion schemes usually do not consider the latter interval, due to the interaction of aerosols, clouds and surface albedo with visible radiation; uncertainties in the knowledge of these latter parameters can lead to very large errors in the modelling of the atmospheric radiative transfer.
Neural Network (NN) algorithms represent an alternative approach for inversion problems in remote sensing. The effectiveness of NN algorithms for the retrieval of ozone concentration profiles from satellite data has been shown in recent studies. Moreover NNs determine the input-output relationship directly form the data, hence an explicit modelling of all radiative interactions can be avoided with this approach.
This paper aims at demonstrating that the use of VIS wavelengths using a Neural Network algorithm can significantly improve the accuracy of ozone retrievals. To achieve this goal we tried two different algorithms, the first trained to retrieve ozone concentration profiles, and the second to directly retrieve tropospheric ozone columns, both from SCIAMACHY Level 1 data. Satellite data were matched with ozonesondes measurements to obtain input-output pairs for the net; we also considered the use of synthetic spectra to enrich the statistics of the datasets. Comparisons between two neural architectures, one using only UV wavelengths and one using both UV and VIS wavelengths, have been made for the two algorithms. The design stage of the Neural Networks, including the preparation of training dataset and input selection by means of an Extended Pruning technique, is here discussed. The algorithms were tested on independent datasets. The results show that, with the use of VIS radiation, the accuracy of the retrievals, especially in the troposphere, can be improved up to the 20%
Characterizing land cover from X-band COSMO-SkyMed images by neural networks
No full textThe launch of last-generation satellites (COSMO-SkyMed and TerraSAR-X), equipped with X-band sensors acquiring images with a very high spatial resolution, has opened up new challenges in the field of SAR image processing for remote sensing applications. In this work, a set of Spotlight and Stripmap COSMO-Skymed images taken the Tor Vergata-Frascati test site was considered to investigate on the potential of this type of data in characterizing sub-urban areas by exploiting both amplitude and phase information contained in the radar return. In particular, this contribution deals with the development of a pixel based classification technique based on Multi-Layer Perceptron (MLP) Neural Networks (NN). The results have been compared with a land cover map of the same area, achieved by means of a different neural network algorithm exploiting the information carried by the eight bands of WorldView-2 satellit
Automatic features extraction in sub-urban landscape using very high resolution Cosmo-Skymed SAR images
No full textThe new generation of spaceborne instruments, capable of capturing a large amount of very-high resolution images within a short revisit time, is allowing remote sensing researchers and final users to receive huge amounts of data in rather short times. Such a scenario makes it mandatory the development of techniques, as much as possible automatic, for the understanding and the effective exploitation of the available information. This contribution deals with the features extraction from Spotlight Cosmo-SkyMed SAR imagery (1 m spatial resolution) by means Multi Layer Perceptron Neural Network (MLP-NN) algorithms. For a better pixel characterization, textural parameters have been also considered as additional information for the classification procedur
Neural networks ensemble for automatic detection of changes from Cosmo-Skymed SAR images
No full textRemote observations in the optical part of the spectrum are generally used to monitor land cover and its changes. However, atmospheric conditions can seriously degrade the performance of optical sensors, which, furthermore, can only operate in daylight. As a consequence, to meet the requirements of promptness, timeliness and reliability, use of synthetic aperture radar (SAR) must be considered. A crucial step forward in Earth observation has been facilitated by the recent (2011) full availability of SARs on the COSMO-SkyMed (CSK) satellite constellation, operated by the Italian Space Agency (ASI). In fact, the four CSK X-band SAR sensors now in orbit are able to provide images not only at 1 m spatial resolution, but also with a very short revisit time, presently as short as 12 hours, irrespective of cloud cover and light conditions. To take advantage of the unique capabilities of the CSK observing system, adequate exploitation of the information contained in the meter-resolution multi-temporal SAR images is necessary. In particular, the large amount of data contained in each image calls for the development of suitable automatic techniques to manage in near-real time the information on land cover changes which are provided by the SAR observations. This paper presents and discusses a novel change detection method, based on the joint use of different neural networks architectures. It is well known that neural networks (NNs) can be very effective in classifying optical and SAR satellite images. Nevertheless, since the relative novelty of the VHR X-band CSK data, their understanding in this case is still under investigation, and only few studies dealing with the land cover characterization and change detection in CSK images have been carried out
Neural network architectures for classification of multi-temporal and multi-angle CHRIS-PROBA data
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