Rong Qu

TL;DR: In this paper, a polarimetric SAR image classification method based on residual learning and a conditional GAN was proposed, which achieved good regional consistence of a classification result image, and high in classification precision.

TL;DR: In this article, a polarimetric SAR image classification method based on DCGAN is proposed, which comprises the following steps: 1) obtaining an odd-order scattering coefficient, an even-order scatter coefficient, and a volume scattering coefficient; 2) normalizing each element value in the characteristic matrix F based on pixel points to [0, 1], and calling a result of normalization as a feature matrix F1; 3) replacing each element in the feature matrixF1 by 64x64 image blocks around each elements, to obtain a feature matrices F2 based on

TL;DR: It is shown that attribute graphs can be used to represent information such as the relations between events and thus can help to retrieve re-usable cases that have similar structures to the new problems.

TL;DR: In this article, a non-subsample contourlet DCGAN-based polarized SAR image classification method is proposed, which consists of the following steps of: inputting a to-beclassified SAR image to carry out Pauli decomposition; forming an image block-based data set by 32*32 blocks by using a normalized dataset; constructing a no-label training dataset, a label training dataset and a test dataset, dividing superpixel blocks for the Pauli decomposed pseudo color graph by utilizing an SLIC superpixel algorithm, and training the non-Subsample cont

TL;DR: In this article, a polarized SAR image object classifying method based on multi-quantum ridgelet representation solves the problem of insufficient feature representation, low classification precision and high time complexity of the prior art.