A commentary and correction on the article “Pansharpening by exploiting sharpness of the spatial structure”

This study clarifies the implicit potential deficiency caused by the sparse cardinality parameter k in Rong et al. (2014). In addition, k = β × W × M × N (0.9 ≤ β < 1) is suggested to avert this potential deficiency, where β is a ratio controlling the amount of sparse cardinality, W is the number of multispectral bands and M × N is the size of panchromatic image. With the choice of k suggested in this study, the low rank matrix L and sparse matrix S obtained by Go Decomposition (Zhou and Tao 2011) can be iteratively optimized and solved. Thus, instead of choosing k as W × M × N in Rong et al. (2014), the potential deficiency that L is directly obtained as an analytic solution can be averted.     

PolSAR image compression based on online sparse K-SVD dictionary learning

we present a novel polarimetric synthetic aperture radar (PolSAR) image compression scheme. PolSAR data contains lots of similar redundancies in single-channel and massively correlation between polarimetric channels. So these features make it difficult to represent PolSAR data efficiently. In this paper, discrete cosine transform (DCT) is adopted to remove redundancies between polarimetric channels, simple but quite efficient in improving compressibility. Sparse K-singular value decomposition (K-SVD) dictionary learning algorithm is utilized to remove redundancies within each channel image. Double sparsity scheme will be able to achieve fast convergence and low representation error by using a small number of sparsity dictionary elements, which is beneficial for the task of PolSAR image compression. Experimental results demonstrate that both numerical evaluation indicators and visual effect of reconstructed images outperform other methods, such as SPIHT, JPEG2000, and offline method.     

Region-driven distance regularized level set evolution for change detection in remote sensing images

Change detection is a fundamental task in the interpretation and understanding of remote sensing images. The aim is to partition the difference images acquired from multitemporal satellite images into changed and unchanged regions. Level set method is a promising way for remote sensing images change detection among the existed methods. Unfortunately, re-initialization, a necessary step in classical level set methods is known a complex and time-consuming process, which may limits their practical application in remote sensing images change detection. In this paper, we present an unsupervised change detection approach for remote sensing image based on an improved region-based active contour model without re-initialization. In order to eliminate the process for re-initialization and reduce the numerical errors caused by re-initialization, we describe an improving level set method for remote sensing images change detection. The proposed method introduced a distance regularization term into the energy function which could maintain a desired shape of the level set function and keep a signed distance profile near the zero level set. The experimental results on real multi-temporal remote sensing images demonstrate the advantages of our method in terms of human visual perception and segmentation accuracy.     

基于改进MC算法的医学图像三维重建研究

MC算法是经典的三维重建方法。但它重建时效率低,产生了大量的三角面片,增加了绘制的时间和空间。而且存在拓扑二义性,会使重建后的图像产生空洞的结构,重建的效果也不是很理想。对此,提出相应的改进策略。介绍了如何提高计算效率、减少三角面片数量、消除二义性和平滑图像等方面。通过实验证明了改进算法的可行性。     

采用混合时钟模式提高Linux时钟精度的方法

时钟精度直接影响到实时任务能否被及时响应和调度,数控系统要求其软件平台能提供微秒级的时钟精度。Linux操作系统由于其开放源代码的特点,非常适合开发具有自主产权的全软件数控系统,但是其毫秒级时钟精度明显过于粗糙。结合上述方法,提出一种混合多种时钟模式的动态时钟系统,提高Linux的时钟精度。通过仿真测试证明能满足数控系统的要求。     

Feature scaling for kernel fisher discriminant analysis using leave-one-out cross validation

Kernel fisher discriminant analysis (KFD) is a successful approach to classification. It is well known that the key challenge in KFD lies in the selection of free parameters such as kernel parameters and regularization parameters. Here we focus on the feature-scaling kernel where each feature individually associates with a scaling factor. A novel algorithm, named FS-KFD, is developed to tune the scaling factors and regularization parameters for the feature-scaling kernel. The proposed algorithm is based on optimizing the smooth leave-one-out error via a gradient-descent method and has been demonstrated to be computationally feasible. FS-KFD is motivated by the following two fundamental facts: the leave-one-out error of KFD can be expressed in closed form and the step function can be approximated by a sigmoid function. Empirical comparisons on artificial and benchmark data sets suggest that FS-KFD improves KFD in terms of classification accuracy.     

常规光纤非线性对色散补偿光纤位置的影响

通过数值计算的方法分析了色散、自相位调制和激光器啁啾等因素对１０Ｇｂ／ｓ１００ｋｍＧ．６５２光纤通信系统的综合效应。对１０Ｇｂ／ｓＧ．６５２光纤系统中用色散补偿光纤（Ｄｉｓｐｅｒｓｉｏｎ Ｃｏｍｐｅｎｓａｔｉｏｎ Ｆｉｂｅｒ,即ＤＳＦ）的两种方案进行了比较,然后简单讨论了色散补偿光纤在密集波分复用（ＤＷＤＭ）中的应用,最后给出通过以上分析得出的结论。     

甚小孔径卫星（VSAT）的数据传输应用

简单分析了甚小孔径卫星通信的技术特点和优缺点。重点讨论用它来解决数据传输问题,通过举例对目前采用ＶＳＡＴ卫星通信解决数据传输问题的技术做进一步的讨论。