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: Understand the problem.
Landuse classification using satellite imagery involves categorizing different land cover types (forest, water, urban, agriculture, etc.) based on spectral signatures. This is a problem of image classification, and several algorithms can be applied.

Step 2: Examine each option. \begin{itemize} \item Option (A): Maximum Likelihood
This is a supervised classification method widely used in remote sensing. It assumes the data follow a normal distribution and classifies pixels based on probability. Hence, this method is valid. \item Option (B): Northwest Corner Method
This method belongs to Operations Research (used for solving transportation problems). It is unrelated to satellite image classification. Therefore, this option is invalid. \item Option (C): K Means
K-Means clustering is an unsupervised classification method, commonly applied in remote sensing to partition pixels into clusters without prior training data. Hence, valid. \item Option (D): ANN (Artificial Neural Networks)
ANNs are advanced machine learning models that can classify satellite images with high accuracy, especially with large datasets. Hence, valid. \end{itemize}

Step 3: Final Selection.
Valid methods are: Maximum Likelihood, K Means, and ANN.

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