By convention in deep learning, an image is commonly stored as a $c \times h \times w$ tensor, where $c$ represents the number of color channels, $h$ is the height, and $w$ is the width. While grayscale images require only a single channel ($c=1$), modern color images typically use three channels (red, green, and blue). More complex data, such as hyperspectral imagery, can contain well over $100$ separate channels.