  This study introduces a novel unsupervised medical image feature extraction
method that employs spatial stratification techniques. An objective function
based on weight is proposed to achieve the purpose of fast image recognition.
The algorithm divides the pixels of the image into multiple subdomains and uses
a quadtree to access the image. A technique for threshold optimization
utilizing a simplex algorithm is presented. Aiming at the nonlinear
characteristics of hyperspectral images, a generalized discriminant analysis
algorithm based on kernel function is proposed. In this project, a
hyperspectral remote sensing image is taken as the object, and we investigate
its mathematical modeling, solution methods, and feature extraction techniques.
It is found that different types of objects are independent of each other and
compact in image processing. Compared with the traditional linear
discrimination method, the result of image segmentation is better. This method
can not only overcome the disadvantage of the traditional method which is easy
to be affected by light, but also extract the features of the object quickly
and accurately. It has important reference significance for clinical diagnosis.
