Satellite image classification using tensorflow in python using cnn

sure! here's a tutorial on satellite image classification using tensorflow in python using convolutional neural networks (cnn).

satellite image classification is the process of categorizing pixels in a satellite image into different land cover classes such as water, vegetation, urban areas, etc. cnns are widely used for image classification tasks due to their ability to capture spatial hierarchies in images.

in this tutorial, we'll build a simple cnn model using tensorflow to classify satellite images.

step 1: import necessary libraries

step 2: load and preprocess satellite image data
before building the cnn model, you'll need to load and preprocess the satellite image data. you can use libraries like `numpy` and `opencv` to load and preprocess the image data.

step 3: build the cnn model

step 4: train the cnn model

step 5: evaluate the model

this is a basic example of how to build a cnn model for satellite image classification using tensorflow in python. depending on the complexity of your dataset, you may need to further optimize the model architecture and hyperparameters for better performance.

i hope this tutorial helps you get started with satellite image classification using tensorflow and cnns! let me know if you have any questions.

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