Now the technical stuff: 3D-2D CNN stands for “three-dimensional convolutional neural network” which is a fancy way of saying we’re using a type of machine learning algorithm called a Convolutional Neural Network (CNN) to analyze images that have more than just one dimension. In this case, the images are hyperspectral, meaning they contain multiple spectral bands or “channels”.

So how does it work? Well, first we feed our data into the CNN and let it do its thing. The CNN will learn to identify patterns in the data that can help us classify them based on their features (like color, texture, etc.). These patterns are called “filters” or “kernels”, and they’re essentially small windows of pixels that slide across the image looking for specific patterns.

The cool thing about CNNs is that they can learn to identify these patterns automatically without us having to tell them exactly what we want to look for. This makes them really powerful tools for analyzing complex data like hyperspectral images, which contain a lot of information but can be difficult to interpret manually.

Now the “feature hierarchy” part: this refers to the fact that CNNs learn features at different levels of abstraction or complexity. For example, in the early stages of the network, it might look for simple patterns like edges and corners. As we move deeper into the network, these features become more complex and abstract (like shapes and textures).

This is important because it allows us to classify images at different levels of detail or resolution. For example, if we’re trying to identify a specific type of vegetation in a hyperspectral image, we might use the early stages of the network to look for simple patterns like edges and corners that are characteristic of that vegetation. Then, as we move deeper into the network, we can use more complex features (like shapes and textures) to help us make a more accurate classification.

In simpler terms: we’re using CNNs to analyze hyperspectral images by looking for patterns at different levels of abstraction or complexity. This allows us to classify these images into different categories based on their features, which can be really helpful in a variety of applications (like agriculture, environmental monitoring, and more).