Imaging spectrometers collect reflected light energy in “bands.” A band represents a segment of the electromagnetic spectrum. Spectral Band.

You can pick and choose which spectral bands to use in your image classification techniques in remote sensing.

…which brings us to our next section – the spectral signature cheatsheet. In remote sensing there are four dimensions, said Navulur, typically spectral, spatial, radiometric and temporal and the combination of these dimensions. This means in your remote sensing classification you will have a higher probability to auto-magically pull out features of interest with spectral signatures. The spectral filters on the WorldView-2 satellites are very accurate, and thus the information you can extract in this spectral band …
A spectral band is a matrix of points defined by three dimensions, its coordinates and the intensity relating to the radiance. Advanced multi-spectral sensors called hyperspectral sensors, detect hundreds of very narrow spectral bands throughout the visible, near-infrared, and mid-infrared portions of the electromagnetic spectrum. Optical remote sensing systems are classified into the following types, depending on the number of spectral bands used in the imaging process.

Related terms: MODIS; Near Infrared; Reflectance; Wavelength; Spatial Resolution; Atmospherics; Aerosol; Remote Sensing

The spectral bands are essential in remote sensing analysis based on the fact that various surface features (vegetation, water, soil, snow, man-made objects) reflect and absorb the electromagnetic radiation from Sun in different ways. Spectral remote sensing data are collected by powerful camera-like instruments known as imaging spectrometers. Using single spectral bands or various band combinations, you can identify and analyze different objects or phenomena. You can think of it as a bin of one “type” of light. Panchromatic imaging system: The sensor is a single channel detector sensitive to radiation within a broad wavelength range. From: Optical Remote Sensing of Land Surface, 2016.