Question

Select the method(s) that can be used for landuse classification based on satellite images.

• A. Maximum Likelihood
• B. Northwest Corner Method
• C. K Means
• D. ANN

Hint:

For landuse classification, focus on algorithms used in remote sensing and image processing (e.g., Maximum Likelihood, K-Means, ANN). Avoid mixing them with unrelated optimization or OR techniques.

Answer 1

The Correct Option is A, C, D

Step 1: Problem Definition. Satellite imagery landuse classification categorizes land cover types (e.g., forest, water, urban, agriculture) using spectral signatures. This is an image classification task, amenable to various algorithms.

Step 2: Option Evaluation. \begin{itemize} \item Option (A): Maximum Likelihood
A supervised classification method common in remote sensing. It classifies pixels based on probability, assuming normal distribution of data. This method is applicable. \item Option (B): Northwest Corner Method
Belongs to Operations Research, specifically for transportation problems. It is irrelevant to satellite image classification and thus invalid. \item Option (C): K Means
K-Means clustering is an unsupervised classification method used in remote sensing to segment pixels into clusters without prior training. This method is valid. \item Option (D): ANN (Artificial Neural Networks)
ANNs are sophisticated machine learning models capable of high-accuracy satellite image classification, particularly with extensive datasets. This method is valid. \end{itemize}

Step 3: Conclusion.
Applicable methods include: Maximum Likelihood, K Means, and ANN.

Final Answer: \[\boxed{(A), (C), (D)}\]