how to give training pixels to the neural network?

Hi, My name is jabeen. I am new to MATLAB and working on the project 'Classification of an image using neural networks'. Here, remotely sensed satellite image is an input to the neural network(RGB Image), the classification of the whole image is to be done(considered as an output of neural network). So, the neural network is to be trained. For this purpose, features are extracted from the image. Now, my query is how to write the code to give the training pixels and their corresponding classes(classification) to the neural network. please help me with this.Explain in detail. Thanks in advance.

Answer 1

[Hari]

Hi Jabeen,

I understand that you are working on a project involving image classification using neural networks in MATLAB, and you want to know how to provide training pixels and their corresponding classes to the neural network.

I assume you have already extracted the features from the remotely sensed satellite image and have a set of labeled data (training pixels and their classes) ready for training the neural network.

In order to train the neural network with your image data, you can follow the below steps:

Prepare the Data:

Organize your extracted features into a matrix X, where each row represents a pixel and each column represents a feature.

Create a vector Y for the corresponding classes of each pixel. Ensure that the classes are in a format suitable for your neural network (e.g., one-hot encoding for classification tasks).

Define the Neural Network:

Use the “patternnet” function to create a feedforward neural network. Specify the number of hidden neurons based on your problem’s complexity.

hiddenLayerSize = 10; % Example size
net = patternnet(hiddenLayerSize);

Configure the Neural Network:

Configure the neural network for training using the “configure” function. This step is optional but helps in setting up the network based on input and target data.

net = configure(net, X', Y');

Train the Neural Network:

Use the “train” function to train the network with your data. This function adjusts the weights and biases to minimize the error.

[net, tr] = train(net, X', Y');

Validate the Neural Network:

After training, validate the network using a separate validation dataset or by checking the performance on the training data itself.

Y_pred = net(X');
performance = perform(net, Y', Y_pred);

Refer to the documentation of the following functions to know more about their properties and usage:

• “patternnet”: https://www.mathworks.com/help/deeplearning/ref/patternnet.html
• “perform”: https://www.mathworks.com/help/deeplearning/ref/perform.html

Hope this helps!