high-resolution-sar

离散傅里叶变换、离散余弦变化、离散小波变换(Discrete Fourier transform, discrete cosine transform and discrete wavelet transform)

基于堆栈的图像分割算法，速度较普通区域生长法更快(Stack-based image segmentation algorithm, region growing faster than the ordinary method faster)

代码实现2014 CAD 文章 Progressive and iterative approximation for least squares B-spline curve and surface fitting(The code implementation of Progressive and iterative approximation for least squares B-spline curve and surface fitting)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)

一个师兄编写的图像配准程序，其中sift代码匹配以及错误点检测值得参考学习。(A brother write image registration procedures, which sift code matches the reference point of error detection is worth learning.)

这是一个很全的云模型图像处理，功能全，已运行(This is a good use of the cloud model program, function, good effect, has been in operation)

图像配准和拼接，肯定可以使用。。。harris+ransac(Image registration and stitching, they can definitely use. . .)

基于LDA降维的遥感图像分类，LDA算法函数程序，可与各种分类算法结合，提高图像分类的速率(remote sensing image cut down dimension algorithm based on LDA.)

SEEDS超像素分割算法的源码，C++实现，可运行(The source code of SEEDS Superpixel picture segmentation)

TERCOM（数字高程匹配）地形匹配算法，经调试可以运行(TERCOM (Digital Elevation match) terrain matching algorithm, the debugger can be run)