tuxiangjibencaozuo

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

龙贝格算法。。。。。。。。。。。 (Romberg algorithm. . . . . . . . . . .)

：滤波 fs=400 采样频率,必须大于两倍基带信号最高频率 ts=1/fs 采样时间间隔 T=5 时间窗大小 t=0:ts:T (hf1=zeros(1,N) hf1(f<=B1&f>=-B1)=1 y1=hf1.*fftshift(y) figure subplot(211) plot(f,abs(fftshift(y))/N,f,hf1, r-- ) axis([-fs/2,fs/2,0,1.2]) title( 原信号频谱 ) xlabel( f /Hz ) subplot(212) plot(f,abs(y1)/N) title( 低通滤波，截止频率90Hz ) xlabel( f /Hz ))

prmlmatlab各章算法程序的编写，这些程序是各大名校的大牛人认真编写，给我们学习MATLAB有很大帮助(prmlmatlab algorithm procedures for the preparation of each chapter, these programs are the major schools of large cattle carefully prepared, give us great help to learn MATLAB)

胡不变矩相关代码，有所需图片和源程序m文件(Invariant moments)

使用Gdal读取DEM地震前后影像高程，并计算地震前后高程差，绘制地形变化(skyline Gdal)

均值滤波,加权滤波,中值滤波的MATLAB实例,用于图象及信号的处理(Mean filter, weighted filtering, median filtering of the MATLAB examples for image and signal processing)

this M - file calculate the gradient of a two dimentional function

将两个矩阵的卷积运算转换为构造的新矩阵的相乘运算(The two matrix of convolution operation into the matrix multiplication)

计算视频图像中每一帧的能量,matlab 代码，可以用于分析图像原能量与编码解码后，误差与原图像能量的倍数关系(computing the energy of each frame in image processing)