Color digital images are made of pixels, and pixels are made of combinations of primary colors represented by a series of codes. A channel in this context is the grayscale image of the same size as a color image, made of just one of these primary colors. For instance, an image from a standard digital camera will have a red, green and blue channel. A grayscale image has just one channel.
In geographic information systems, channels are often referred to as raster bands. Another closely related concept is feature maps, which are used in convolutional neural networks.
In the digital realm, there can be any number of conventional primary colors making up an image; a channel, in this case, is extended to be the grayscale image based on any such conventional primary color. By extension, a channel is any grayscale image with the same size as the "proper" image and associated with it.
"Channel" is a conventional term used to refer to a certain component of an image. In reality, any image format can use any algorithm internally to store images. For instance, GIF images actually refer to the color in each pixel by an index number, which refers to a table where three color components are stored. However, regardless of how a specific format stores the images, discrete color channels can always be determined, as long as a final color image can be rendered.
The concept of channels is extended beyond the visible spectrum in multispectral and hyperspectral imaging. In that context, each channel corresponds to a range of wavelengths and contains spectroscopic information. The channels can have multiple widths and ranges.
Three main channel types (or color models) exist and have respective strengths and weaknesses.