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A novel deep learning model for predicting marine pollution for sustainable ocean management. [PDF]

PeerJ Comput Sci
Edeh MO, Dalal S, Alhussein M, Aurangzeb K, Seth B, Kumar K.   +5 more
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PolSAR image classification using discriminative clustering

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), 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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PolSAR image classification using generalized scattering models

2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL), 2017
In this paper, we present a new model-based method for polarimetric synthetic aperture radar (PolSAR) image classification. The conventional single- and double-bounce scattering models do not have contributions on T 33 element of coherency matrix. The T 33 element of coherency matrix accounts for the cross-polarization power.
H. Maurya, R. K. Panigrahi
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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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Recursive Convolution Neural Network for PolSAR Image Classification

2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2020
Considering the limited of the training samples and the effect of speckle noise in PolSAR images, which further affects the learning performance of the classifier, a recursive convolution neural network model (CNN) is proposed. Samples with high confidence of each classification result will be used as the training samples of the next training.
Shuyin Zhang, Xudong Jing, Hongming Zhang, Huan Chen, Jianbang Zhao   +4 more
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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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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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Task-Oriented GAN for PolSAR Image Classification and Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2019
Based on a generative adversarial network (GAN), a novel version named Task-Oriented GAN is proposed to tackle difficulties in PolSAR image interpretation, including PolSAR data analysis and small sample problem. Besides two typical parts in GAN, i.e., generator (G-Net) and discriminator (D-Net), there is a third part named TaskNet (T-Net) in the Task ...
Fang Liu, Licheng Jiao, Xu Tang
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