mage classification refers to the task of extracting information classes from a multiband raster image. The resulting raster from image classification can be used to create thematic maps. Depending on the interaction between the analyst and the computer during classification, there are two types of classification: supervised and unsupervised.
Convolutional Neural Networks come under the subdomain of Machine Learning which is Deep Learning. Algorithms under Deep Learning process information the same way the human brain does, but obviously on a very small scale, since our brain is too complex (our brain has around 86 billion neurons).
Step by Step Guide
Step 1: Choose a Dataset
Step 2: Prepare Dataset for Training
Step 3: Create Training Data
Step 4: Shuffle the Dataset
Step 5: Assigning Labels and Features
Step 6: Normalising X and converting labels to categorical data
Step 7: Split X and Y for use in CNN
Step 8: Define, compile and train the CNN Model
Step 9: Accuracy and Score of model
Resourses:-https://www.tensorflow.org/tutorials/images/classification
used library like
tensorflow,
pandas
numpy
keras
pytorch
plotly
seaborn
Thank you