Image normalization:
Normalization refers to converting the pixel values of remote sensing images into a specific range, such as [0,1] or [0,255]. The purpose of this is to facilitate the comparison and analysis of pixel values between different images. Commonly used normalization methods are as follows:
1. Maximum and minimum normalization: Divide the pixel value by the difference between the maximum and minimum values that occur in the image.
2. Mean variance normalization: Subtract the mean value from the pixel value and divide it by the standard deviation, so that the average value of the pixel value is 0 and the variance is 1.
3. Percentage linear stretching: Linearly convert pixel values to a certain percentage range, for example, set the output value corresponding to 1% of the pixel values in the image to 0, and set the output value corresponding to 99% of the pixel values to 255.
The choice of the above methods depends on the specific application scenarios and data characteristics.
Image normalization:
Image normalization is a method of normalizing image data, which aims to eliminate the influence of factors such as brightness and contrast in the image on image analysis and comparison. In the field of medical image analysis, image standardization is one of the commonly used data preprocessing methods, which can make images taken from different sources, different devices, and at different times comparable.
The specific steps of image standardization include: 1) Determine the target of standardization, such as the range, average, variance, etc. of pixel values; 2) Extract image features, such as histogram, gray level, etc.; 3) Perform conversion or adjustment, such as linear Or nonlinear transformation, etc.; 4) Apply standardization methods, such as local or global standardization.
Common image normalization methods include: histogram equalization, Z-score normalization, white hat and black hat transformation, grayscale stretching, etc. Image standardization can improve image quality and reliability to better support medical diagnosis, research, and treatment.
Image Normalization and Normalization
For machine learning, it is recommended to normalize or normalize the features. The code below shows how to implement these feature scaling techniques.
function:
ee.Reducer.