Vineesh V, Geography

Minimum distance to means classification is a supervised classification technique in remote sensing that works by dividing the data into a number of classes based on the mean value of each class. The algorithm works as follows: First, the mean value of each class is calculated. This is done by taking the average of all the data points in each class. Next, for each data point, the distance to the mean of each class is calculated. This is done using a distance metric, such as Euclidean distance. The data point is then assigned to the class with the minimum distance to the mean. This process is repeated for all data points in the dataset. Minimum distance to means classification is simple and easy to implement, but it can be sensitive to noise and outliers in the data. It is generally not as accurate as more complex classification algorithms, such as support vector machines or neural networks. 2. Gaussian maximum likelihood classification is a method of image analysis in remote sensing th...