Method of extracting image of port wharf through multispectral interpretation

A method of extracting an image of a port wharf through multispectral interpretation, comprising the following steps:

• S100:
• assigning values to grayscale values to divide a coastline:
• performing a grayscale processing on an original remote sensing image, assigning the values to the grayscale values, and extracting a blurred coastline according to a distribution of the grayscale values;
• S200:
• smoothing the original remote sensing image and removing noise:
• performing a smoothing processing on an area of the blurred coastline in the original remote sensing image according to a range limited by the blurred coastline and removing interference noise to extract edge information;
• S300:
• establishing a multispectral database of a targeted port wharf:
• linearly combining multivariate observed values among different edge information, regularizing a linear combination to obtain a kernel function, as multispectral data, and repeating to obtain the multispectral database,
• wherein an MAF transformation is performed on an image in an edge region before an operation of the linear combination is performed, in order to obtain an autocorrelation factor, and a specific algorithm is as follows:
• αTx(r) is set as the autocorrelation factor, x(r) as a first multivariate observed value at a point r, x(r+δ) as a second multivariate observed value at a point r+δ, and δ as a spatial displacement vector, then an auto-covariance R of a linear combination αTx(r) of x(r) is calculated according to the following formula:
• R=Cov{αTx(r), αTx(r+δ)}, and the autocorrelation factor is obtained after an inverse operation is performed on the auto-covariance R;
• the linear combination and a regularization of the linear combination comprises the following steps:
• S301:
• transforming the auto-covariance R to obtain the following equation:
• αTCδα=αT(Cδ+CδT)α/2, where Cδ is a transformation-related matrix;
• S302:
• setting, an autocorrelation coefficient ρ of the linear combination as follows:
• ρ=1−(αTSδα)/(2αTSα), where a difference covariance matrix Sδ is calculated according to the following formula:
• Sδ=2S−(C67 +CδT), and S=XTX/(n−1) is set as a covariance matrix of x:
• and S303:
• selecting an optional form of the autocorrelation coefficient as following:
• ρ=1−½[(αTXTAα)/(αT[(1−k)XδTXδ+kIp)]α]−1, transforming the optional form to obtain a kernel function form as following:
• ρ=1−½[(bTK2b )/(bT[(1−k)KδK67 T+kK)]b]−1, where XTb=α, A is a transformation factor, k is a transformation coefficient, Ip is a unit vector of a eigenvector P, and K is a correlation matrix of the transformation coefficient:
• and
• S400:
• extracting a port wharf using a projected eigenvector:
• performing an MAF transformation on the kernel function after being regularized, projecting the multivariate observed values to original eigenvectors, and identifying a remote sensing image area corresponding to a validated original eigenvector as a port wharf to be extracted.