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说明：  结合模糊C均值聚类算法与水平集方法的图像分割代码，分割效果良好。(The image segmentation code based on fuzzy c-means clustering algorithm and level set method has a good segmentation effect.)

k均值算法的matlab程序，可直接运行。(k-means algorithm matlab procedures, can be directly run.)

用Matlab来实现PCA，并分别求出图像在第一、二、三主分量上的投影。(Using Matlab to implement PCA, and images were obtained in the first, second and third principal component on the projection.)

利用核函数进行遥感图像的分类，本程序再支持向量机的基础上引入了核函数进行图像分类，是分类效果更加明显(Use of nuclear function remote sensing image classification, the program no longer support vector machine based on the introduction of the kernel function for image classification, classification effect is more pronounced)

实现图像的几何变换，包括图像的平移、镜像变换、缩放、转置以及旋转。(Achieve geometric transformation of image, including the image translation, image transformation, scaling, transpose and rotation.)

说明：  hough 变换提取直线 MATLAB程序 可以很好的检测出一副图像中的直线(Extraction of straight line hough transform MATLAB program can detect a very good image of a straight line)

一种基于模糊集增强的图像处理算法，可以降噪和锐化图像(Fuzzy set enhanced image processing algorithm, noise reduction and can sharpen the image)

SIFT，即尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点，是一种局部特征描述子。 该方法于 1999 年由 David Lowe 首先发表于计算机视觉国际会议（International Conference on Computer Vision，ICCV），2004 年再次经 David Lowe 整理完善后发表于 International journal of computer vision（IJCV） 。截止 2014 年 8 月，该论文单篇被引次数达 25000 余次。(来自百度)(SIFT, namely Scale-invariant feature transform (SIFT), is a description used in the field of image processing. This description has scale invariance and can detect key points in images. It is a local feature descriptor. This method was first published by David Lowe at the International Conference on Computer Vision (ICCV) in 1999 and published in the International Journal of Computer Vision (IJCV) in 2004 after it was sorted out and perfected by David Lowe again. As of August 2014, more than 25,000 citations have been cited. (From Baidu))

对图片预处理加入高斯噪声，各种经典边缘检测算子对图像进行边缘提取(Gaussian noise on the image pre-processing, a variety of classic edge detection operator to extract image edge)

说明：  双峰法阈值分割。阈值分割法是一种基于区域的图像分割技术，原理是把图像象素点分为若干类。图像阈值化分割是一种传统的最常用的图像分割方法，因其实现简单、计算量小、性能较稳定而成为图像分割中最基本和应用最广泛的分割技术。它特别适用于目标和背景占据不同灰度级范围的图像。它不仅可以极大的压缩数据量，而且也大大简化了分析和处理步骤，因此在很多情况下，是进行图像分析、特征提取与模式识别之前的必要的图像预处理过程。图像阈值化的目的是要按照灰度级，对像素集合进行一个划分，得到的每个子集形成一个与现实景物相对应的区域，各个区域内部具有一致的属性，而相邻区域不具有这种一致属性。这样的划分可以通过从灰度级出发选取一个或多个阈值来实现。(Threshold segmentation)