I was introduced to machine learning and remote sensing recently. My task was to classify the satellite images into vegetation and non vegetation. We were introduced to two approaches.

1. Supervised learning - where we had wkt or geojson files made from ground truth. These files had polygons which were used to train the model. satellite images from WorldView-3 Satellite Sensor

2. Unsupervised classification where the pixels were classified based on NDVI values using clustering models such as K-means, Fuzzy C-means clustering. satellite images from landsat 8

While all of these things were virtually spoon fed and I took the code samples from here and there. I still fail to understand which method is used where, specifically with context of crop forecasting?

What is the advantage of collecting the ground truth, when we can use the unsupervised learning to classify the images. If it is about accuracy, then are there any specific examples as to how ground truth helps in accuracy in crop forecasting?

I was introduced to machine learning and remote sensing recently. My task was to classify the satellite images into vegetation and non vegetation. We were introduced to two approaches.

1. Supervised learning - where we had wkt or geojson files made from ground truth. These files had polygons which were used to train the model. satellite images from WorldView-3 Satellite Sensor
2. Unsupervised classification where the pixels were classified based on NDVI values using clustering models such as K-means, Fuzzy C-means clustering. satellite images from landsat 8

While all of these things were virtually spoon fed and I took the code samples from here and there. I still fail to understand which method is used where, specifically with context of crop forecasting.

What is the advantage of collecting the ground truth, when we can use the unsupervised learning to classify the images?

If it is about accuracy, then are there any specific examples as to how ground truth helps in accuracy in crop forecasting?

I was introduced to machine learning and remote sensing recently. My task was to classify the satellite images into vegetation and non vegetation. We were introduced to two approaches.

1. Supervised learning - where we had wkt or geojson files made from ground truth. These files had polygons which were used to train the model. satellite images from WorldView-3 Satellite Sensor

2. Unsupervised classification where the pixels were classified based on NDVI values using clustering models such as K-means, Fuzzy C-means clustering. satellite images from landsat 8

While all of these things were virtually spoon fed and I took the code samples from here and there. I still fail to understand which method is used where, specifically with context of crop forecasting?

I was introduced to machine learning and remote sensing recently. My task was to classify the satellite images into vegetation and non vegetation. We were introduced to two approaches.

1. Supervised learning - where we had wkt or geojson files made from ground truth. These files had polygons which were used to train the model. satellite images from WorldView-3 Satellite Sensor

2. Unsupervised classification where the pixels were classified based on NDVI values using clustering models such as K-means, Fuzzy C-means clustering. satellite images from landsat 8

While all of these things were virtually spoon fed and I took the code samples from here and there. I still fail to understand which method is used where, specifically with context of crop forecasting?

What is the advantage of collecting the ground truth, when we can use the unsupervised learning to classify the images. If it is about accuracy, then are there any specific examples as to how ground truth helps in accuracy in crop forecasting?