geo_feature_image

说明：  生成差异图，用于变化检测，包含比值法、差值法、均值比算法等(The difference graph is generated for change detection, including ratio method, difference method and mean ratio algorithm)

用于BMP图像文件的滚动显示，带上下和水平滚动条。(This file is used to display BMP image files, with up-down and horizontal scroll bars.)

sift算法，特征点的提取与匹配，图像融合，图像拼接。测试过，效果不错，特征点提取较稳定。(Sift u7B97 u6CD2 uFF0C u7279 u5F1 u70B9 u7684 u63D0 u53D6 u4E0E u5339 u914D uFF0C u56FE u50CF u878D u5408 uFF0C u56FE u50CF u62FC u63A5 u3002 u6D4B U8BD5 u8FC3 uFF0C u6548 u679C u4E0D u9519 uFF0C u7279 u5F81 u70B9 u63D0 u53D6 u8F83 u7A33 u5B9A u3002)

粒子群算法的Pareto多目标函数优化，适用于多目标问题的寻优(Particle swarm optimization Pareto multi-objective function optimization, suitable for multi-objective optimization.)

光线补偿 的matlab程序代码，有图片(Light compensation matlab code, pictures)

极坐标图像转直角坐标图像，相当于球面的展开效果，最好是正方形图像，但是长方形也可以。(POLARTOIM converts polar image to rectangular image)

说明：  脉冲压缩技术是匹配滤波理论和相关接收理论的一个很好的实际应用。它的提出很好的解决了这样的一个问题：在发射端发射大时宽、带宽信号，以提高信号的速度测量精度和速度分辨力，而在接收端，将宽脉冲信号压缩为窄脉冲，以提高雷达对目标的距离分辨精度和距离分辨力(Pulse compression technology is a good practical application of matched filtering theory and related receiving theory. It solves such a problem very well: transmitting large time-width and bandwidth signals at the transmitter to improve the speed measurement accuracy and speed resolution of the signal, and compressing the wide pulse signal into narrow pulse at the receiver to improve the range resolution accuracy and range resolution of radar to target.)

Camshift算法，即"Continuously Adaptive Mean-Shift"算法，是一种自适应的运动跟踪算法。它主要通过视频图像中运动物体的颜色信息来达到跟踪目的。(Camshift algorithm, namely " Continuously Adaptive Mean-Shift" algorithm is an adaptive motion tracking algorithm. It is primarily the color information of the object' s movement through the video images to achieve tracking purposes.)

输入一组数据，xy值，用来拟合椭圆。只要改变输入数据，该代码就可以直接使用。另附上椭圆拟合算法。(Enter a set of data, xy values, used to fit ellipses. Just change input data, the code can be used directly. Attached on the ellipse fitting algorithm.)

利用matlab实现数字图象处理的理想低通滤波器！！(Using matlab to achieve the desired low-pass filter! !)