The process of supervised classification of satellite images generally follows these steps:
1. Radiometric/Geometric correction (iv): This is the first step, where the image is corrected for any distortions due to sensor errors, atmospheric conditions, and geometric misalignments. This ensures that the image is accurate and aligned with the Earth's surface.
2. Training (ii): In this step, the user selects training areas for each class of interest. These training areas are used to train the classification algorithm to recognize patterns in the image.
3. Classification (i): Once the training data is ready, the classification process begins. The image is classified into various categories based on the training data, producing the final classified image.
4. Accuracy assessment (iii): After classification, it is essential to assess the accuracy of the classification. This is done by comparing the classification results with ground truth data to determine how well the classification matches real-world conditions.
Thus, the correct sequence is (iv), (ii), (i), (iii), as outlined in option (B).