ID 原文 译文
21125 通过该方法，能够实现子带信号的相干合成，提升了SAR数据成像质量。 Based on the proposed method, the SAR image is improved.
21126 实验数据的处理结果验证了该方法的有效性。 The effectiveness of the method is verified by processing the realSAR data.
21127 该文提出一种基于判别式聚类框架的非监督极化SAR图像分类算法，利用判别式监督分类技术实现非监督聚类。 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.
21128 为实现该算法，定义了一个结合softmax回归模型和马尔科夫随机场光滑性约束的能量函数。 To implement this idea, an energy function is designed for unsupervised PolSAR image classification by combining a supervised Softmax Regression (SR) model with a Markov Random Field (MRF) smoothness constraint.
21129 该模型中，像素类标和分类器均为需要优化的未知变量。 Inthis model, both the pixelwise class labels and classifiers are taken as unknown variables to be optimized.
21130 该算法从基于目标极化分解和K-Wishart极化统计分布而产生的初始化类标开始，交替迭代优化分类器和类标的能量函数，从而实现对分类器和类标的求解。 Starting from the initialized class labels generated by Cloude-Pottier decomposition and K-Wishart distribution hypothesis, the classifiers and class labels are iteratively optimized by alternately minimizing the energy function with respect to them. Finally, the optimized class labels are taken as the classification result, and the classifiers for different classes are also derived as a side effect.
21131 真实极化SAR数据上的实验结果证明了该算法的有效性和先进性。 This approach is applied to real PolSAR benchmark data. Extensive experiments justify that the proposed approach can effectively classify the PolSARimage in an unsupervised way and produce higher accuracies than the compared state-of-the-art methods.
21132 针对目前协同显著性检测问题中存在的协同性较差、误匹配和复杂场景下检测效果不佳等问题，该文提出一种基于卷积神经网络与全局优化的协同显著性检测算法。 To solve the problems in current co-saliency detection algorithms, a novel co-saliency detection algorithm is proposed which applies fully convolution neural network and global optimization model.
21133 首先基于VGG16Net构建了全卷积结构的显著性检测网络，该网络能够模拟人类视觉注意机制，从高级语义层次提取一幅图像中的显著性区域； First, afully convolution saliency detection network is built based on VGG16Net. The network can simulate the humanvisual attention mechanism and extract the saliency region in an image from the semantic level.
21134 然后在传统单幅图像显著性优化模型的基础上构造了全局协同显著性优化模型。该模型通过超像素匹配机制，实现当前超像素块显著值在图像内与图像间的传播与共享，使得优化后的显著图相对于初始显著图具有更好的协同性与一致性。 Second, based on the traditional saliency optimization model, the global co-saliency optimization model is constructed, which realizes the transmission and sharing of the current superpixel saliency value in inter-images and intra-image through superpixel matching, making the final saliency map has better co-saliency value.