First, what is semantic segmentation? It involves assigning each pixel of an input image with a specific label that represents an object or category present in the scene. For example, if we have a satellite image and want to identify different types of land cover such as forests, water bodies, urban areas, etc., semantic segmentation can help us do this by providing a detailed map of these categories for each pixel in the image.

Now how U-Net works specifically for satellite images. It is a deep learning model that consists of two main parts: an encoder and a decoder. The encoder extracts features from the input image using convolutional layers, max pooling, and other techniques to reduce spatial size and increase depth (i.e., number of filters). These features are then passed through several downsampling operations to further reduce their spatial size and increase their depth.

At this point, we have a set of feature maps that represent different levels of abstraction in the input image. For example, one feature map might contain information about edges or boundaries between objects, while another might capture more global features such as texture or color. These feature maps are then fed into the decoder to reconstruct the original image with higher resolution than before using transposed convolutions (also known as deconvolution).

To preserve important details and improve segmentation accuracy, skip connections are added between corresponding layers in the encoder and decoder. These connections allow information from earlier stages of processing to be propagated back through the network, which can help improve segmentation accuracy by providing more context for each pixel. Finally, a softmax activation function is applied to each pixel in the output image to produce a probability distribution over different classes (i.e., land cover categories). This allows us to assign labels to each pixel based on its most likely category.

Here’s an example of how U-Net might segment a satellite image:

In this example, we can see that U-Net has successfully segmented the image into different categories such as forests (green), water bodies (blue), urban areas, etc., using semantic segmentation techniques.