A Polarized SAR Classification Method Based on Semi-Supervised Convolutional Neural Network

A technology of convolutional neural network and classification method, applied in the field of polarization SAR classification based on semi-supervised convolutional neural network, can solve the problems of large demand and low classification accuracy of label data

Active Publication Date: 2019-08-27
XIDIAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide a polarization SAR classification method based on a semi-supervised convolutional neural network to improve the accuracy of ground object classification and solve the problems of the traditional supervised convolutional neural network. The network has a large demand for labeled samples, and the technical problem of low classification accuracy when having low-label data

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  • A Polarized SAR Classification Method Based on Semi-Supervised Convolutional Neural Network
  • A Polarized SAR Classification Method Based on Semi-Supervised Convolutional Neural Network
  • A Polarized SAR Classification Method Based on Semi-Supervised Convolutional Neural Network

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Embodiment 1

[0113] Input the polarization SAR surface object simulation data to be classified, see image 3 (a), input the Pauli decomposition diagram of the polarimetric SAR and the coherence matrix T of the polarimetric SAR image, and obtain the label matrix Y according to the distribution information of the polarimetric SAR image, see image 3 (b), image 3 (b) is the image directly generated by the label matrix Y. Different color blocks in the image represent different ground features. The distribution of the same kind of ground features is represented by the same category label in the label matrix. The distribution of ground features that cannot be determined is in The label matrix is ​​represented by 0, and the sample is generated by the coherence matrix T of the polarimetric SAR image combined with the Pauli decomposition diagram of the polarimetric SAR N is the total number of samples, x i represents the i-th sample.

[0114] Among them, the sample data are randomly extracted ...

Embodiment 2

[0128] 1. Experimental conditions

[0129] The hardware platform is: Intel(R) Core(TM) i5-2410M CPU@2.30GHz, RAM 4.00GB;

[0130] The software platform is: MATLAB R2016b;

[0131] The experiment selects 300×270 part of the polarized SAR ground object real data in the Flevoland area of ​​the Netherlands for testing, and the number of categories is 6, namely Bare soil, Potato, Beet, Pea, Wheat and Barley. In the experiment, 1% of samples of each class are randomly selected as training samples, and the rest are testing samples.

[0132] 2. Experimental content and results

[0133] The present invention combines the Softmax classifier to classify the real data of polarized SAR surface objects, and compares it with other deep learning methods under the same experimental setting, wherein CNN is a convolutional neural network, Figure 4 (c) is determined by CNN Figure 4 (a) The result map of the classification; the experiment also uses the deep belief network WDBN based on Wisha...

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Abstract

The invention discloses a polarimetric SAR classification method based on a semi-supervised convolutional neural network. According to the method, first, image data is input, and super pixel segmentation is performed; training samples and test samples are extracted; a neighborhood preserving and sparse filtering deep learning network (NDSFN) is adopted to perform unsupervised pre-training on parameters of the convolutional neural network, and a needed filter set is obtained; a feature map is obtained through convolution operation; fuzzy processing is performed on the feature map through downsampling; parameter settings of the NDSFN are adjusted according to the filter size in a second convolution layer to obtain a new feature map; a Softmax classifier is utilized to perform image classification on the training samples; and a small quantity of marked samples are adopted for slight adjustment to obtain a polarimetric SAR terrain classification. Through the method, by constructing a novelsemi-supervised convolutional neural network (SNCNN) model, the problem that a traditional supervised convolutional neural network has a high requirement for marked samples is effectively solved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a polarimetric SAR classification method based on a semi-supervised convolutional neural network, which can be used in environmental monitoring, earth resource surveying, military systems, and the like. Background technique [0002] Machine learning (Machine Learning, ML) is a multi-field interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. Specializes in the study of how computers simulate or implement human learning behaviors to acquire new knowledge or skills, and reorganize existing knowledge structures to continuously improve their performance. In the field of polarimetric SAR image classification, there have been many breakthroughs in machine learning, such as Wishart maximum likelihood (WML), support vector machines (support vector machines, SVM) a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
Inventor 刘红英王志杨淑媛焦李成慕彩虹熊涛王桂婷冯婕朱德祥
Owner XIDIAN UNIV
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