Results 221 to 230 of about 3,590 (264)

Unsupervised learning rules for POLSAR images analysis

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, 2003
It has been shown (see Chitroub, S. et al., Signal Processing, vol.82, no.1, p.69-92, 2002) that the model for POLSAR (polarimetric synthetic aperture radar) images is a mixture model that results from the product of two distributions, one characterizes the target response and the other characterizes the speckle phenomenon.
S. Chitroub, A. Houacine, B. Sansal
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A four-component decomposition of POLSAR image

Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., 2005
Abstract : A four-component scattering model is proposed to decompose polarimetric synthetic aperture radar images. The covariance matrix approach is used to deal with the non-reflection symmetric scattering case. This scheme includes and extends the three-component decomposition method dealing with the reflection symmetry condition that the co-pol and
Y. Yamaguchi, M. Ishido, H. Yamada, T. Moriyama   +3 more
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PolSAR image classification based on deorientation theory

Proceedings of 2011 IEEE CIE International Conference on Radar, 2011
The randomly distributed target orientation causes confusion in classification of the polarimetric scattering target. In this paper, we proposed an unsupervised classification method to the fully polarimetric SAR (PolSAR) image to solve this problem.
null Guo Rui, null Zang Bo, null Zhang Shuangxi, null Xing Mengdao   +3 more
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Exploring Convolutional Lstm for Polsar Image Classification

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important applications in Pol-SAR image processing. More and more deep learning methods are applied to PolSAR image classification. As we know, the polarimetric response of a target is related to the orientation of the target, but the features in rotation domain are ...
Lei Wang, Xin Xu, Hao Dong, Rong Gui, Rui Yang, Fangling Pu   +5 more
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Effect of apodization on PoLSAR image decomposition

2013 IEEE Applied Electromagnetics Conference (AEMC), 2013
In this paper we study the effect of nonlinear apodization techniques on polarimetric syntehtic aperture radar (PolSAR) image decomposition. We show that sidelobe suppression in PolSAR images helps in better information extraction through target decomposition techniques, and hence results in better target identification.
Rajib Kumar Panigrahi, Amit Kumar Mishra
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POLSAR image factorization and its extended applications

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
POLSAR image factorization is proposed as an extension of polarimetric SAR incoherent target decomposition. It simultaneously estimates a dictionary of meaningful atom scatterers and their corresponding spatial distribution maps from POLSAR image.
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Target Detection beneath Canopy Using PolSAR Images

PIERS Online, 2009
Polarization information is applied to detect target underneath forest by synthetic aperture imaging. At flrst, hard-in-loop PolSAR system is constructed in an anechoic chamber, then the resolution and sampling interval of system are analyzed. In order to obtain accurate polarization data, polarization scattering matrix measurement and calibration ...
Chu-Feng Hu, Jia-Dong Xu, Nan-Jing Li, Lin-Xi Zhang   +3 more
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Multitemporal multidimensional speckle filtering of PolSAR images

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
The combination of the polarimetric information with time observations represents an extremely valuable tool for the different applications of polarimetric SAR images. One of the most important preprocessing steps in this context is speckle filtering.
Maryam Salehi, Ali Mohammadzadeh, Yasser Maghsoudi   +2 more
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Wishart Deep Stacking Network for Fast POLSAR Image Classification

IEEE Transactions on Image Processing, 2016
Inspired by the popular deep learning architecture, deep stacking network (DSN), a specific deep model for polarimetric synthetic aperture radar (POLSAR) image classification is proposed in this paper, which is named Wishart DSN (W-DSN). First of all, a fast implementation of Wishart distance is achieved by a special linear transformation, which speeds
Licheng Jiao, Fang Liu
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Unsupervised PolSAR Image Classification Using Discriminative Clustering

IEEE Transactions on Geoscience and Remote Sensing, 2017
This paper presents a novel unsupervised image classification method for polarimetric synthetic aperture radar (PolSAR) data. The proposed method is based on a discriminative clustering framework that explicitly relies on a discriminative supervised classification technique to perform unsupervised clustering. To implement this idea, we design an energy
Haixia Bi, Jian Sun, Zongben Xu
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