In this paper, a classification scheme is proposed based on deep learning algorithm, and a hierarchical structure is used to classify data based on the deep learning classifier and polarimetric feature selection when there are a variety of land covers.
Multi-look polarimetric SAR (synthetic aperture radar) data can be represented either in Mueller matrix form or in complex covariance matrix form. The latter has a complex Wishart distribution. A maximum likelihood classifier to segment polarimetric SAR data according to terrain types has been developed based on the Wishart distribution.
The authors use Cloude and Pottier's method to initially classify the polarimetric SAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The classified results are then used to define training sets for the next iteration. Significant improvement has been observed in iteration.
Fuzzy Superpixels for Polarimetric SAR Images Classification. Abstract: Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a specific application can improve the performance of the application.
An entropy based classification scheme for land applications of polarimetric SAR.
As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and comparing fully and compact polarimetric satellite synthetic aperture radar (SAR) modes to detect hydrocarbon slicks over the sea surface, discriminating them from weak-damping surfactants, such as biogenic slicks.
SAR image classification with high frequency component derived from wavelet Multi-Resolution Analysis: MRA is proposed. Although it is well known that polarization signature derived from fully polarized SAR data is useful for SAR image.
Classification using the rich information provided by time-series and polarimetric Synthetic Aperture Radar (SAR) images has attracted much attention. The key point is to effectively reveal the correlation between different dimensions of information and form a joint feature. In this paper, a multi-dimensional SAR descriptive primitive for each single pixel is firstly constructed, which in the.
This section provides access to a wide-ranging tutorial, which aims to provide a grounding in polarimetry and polarimetric interferometry with a view to stimulating research and development of scientific applications that exploit such techniques. The tutorial material is made available in PDF format and is also bundled with the software itself.
In polarimetric synthetic aperture radar (PolSAR) image processing, the number of classes is an important factor for PolSAR image classification. Therefore, how to accurately estimate the number of.
This interferometric polarimetric SAR (PolInSAR) multi-chromatic analysis (MCA-PolInSAR) signal processing method permits the efficient separation of oriented buildings from vegetation yielding considerably improved results in which oriented urban areas are recognized, from volume scattering, as double-bounce objects.
Polarimetric SAR images acquired at C- and L-band over sea ice in the Greenland Sea, Baltic Sea, and Beaufort Sea have been analysed with respect to their potential for ice type classification. The.
Automated Sea Ice Classification on dual-polarimetric TerraSAR-X data Rudolf Ressel (Maritime Security Research, DLR). P. SAR polarimetry for sea ice classification. In: Applications of SAR Polarimetry and Polarimetric Interferometry. 2003. S. 18.
In this paper, we design a novel unsupervised architecture for automatic classification of the dominant polarization in polarimetric SAR images. To this end, we leverage the ideas developed in and suitably exploit them to build a decision logic capable of recognizing the dominant scattering mechanism which characterizes the pixel under test.
Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty Yu, P, Qin, K and Clausi, D 2012, 'Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty', IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 4, pp. 1302-1317.Unsupervised classification of SAR imagery using polarimetric decomposition to preserve scattering characteristics Ramakalavathi Marapareddy, James V. Aanstoos, Nicolas H. Younan.Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered.