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一个很好的PCA程序。它可用于数据的降维，消噪及特征提取。(A good PCA procedures. It can be used for data dimensionality reduction, de-noising and feature extraction.)

sar 图像边缘检测算子，代码简洁，能够运行成功。(sar image edge detection，it s simple and useful 。)

多类图像特征结合上下文信息进行目标识别（分类、分割）(Many types of image features combined with context information for target identification (classification, segmentation))

深度学习相关代码实现，可以用来做去噪，去模糊，回归，识别等( Initialize and train an RBM energy-based model. Supports binary, Gaussian, and multinomial inputs (still in development). Supports binary and noisy rectified linear units (NReLU) in the hidden layer. Supports joint modeling of binary and multinomial variables for classification.)

分别使用灰度值变换和频域滤波的方法实现图像的伪彩色处理。在Matlab中编程实现(The use of gray value, respectively, transform and frequency domain filtering method of pseudo-color image processing. Programming in Matlab in)

数字图像处理(Digital Image Processing)是通过计算机对图像进行去除噪声、增强、复原、分割、提取特征等处理的方法和技术。 [1] 数字图像处理的产生和迅速发展主要受三个因素的影响：一是计算机的发展；二是数学的发展（特别是离散数学理论的创立和完善）;三是广泛的农牧业、林业、环境、军事、工业和医学等方面的应用需求的增长(Digital Image Processing (DIP) is a method and technology of removing noise, enhancing, restoring, segmenting and extracting features from images by computer. [1] The emergence and rapid development of digital image processing are mainly influenced by three factors: first, the development of computer; second, the development of mathematics (especially the establishment and improvement of discrete mathematics theory); third, the growth of the demand for extensive applications in agriculture, animal husbandry, forestry, environment, military, industry and medicine.)

使用边缘跟踪，种子填充等算法提取图像边界，然后使用并行的方法来计算图像的距离图。(using edge tracking and seed filling algorithm to detect the edges of images, then use the parallel approach to the calculation of the distance between the image map.)

非线性CS算法,提高SAR成像的精度，弥补CSA和RDA的不足(Nonlinear Chirp Scaling Algorithm)

说明：  matlab使用regionprops求面积，找到连通域(Matlab uses regionprops to calculate area and find connected region)

说明：  DOG高斯差分实现物体识别中的特征提取,对不同尺度空间的图像进行差分，从而获得描述物体的特征值(difference of gaussian to achieve the feature point exaction of object recognition)