Detect a known target in the hyperspectral image by using spectral matching method. The pure spectral signature of the known target material is used to detect and locate the target in a hyperspectral image. In this example, you will use the spectral angle mapper (SAM) spectral matching method to detect man-made roofing materials (known target) in a hyperspectral image. The pure spectral signature of the roofing material is read from the ECOSTRESS spectral lbrary and is used as the reference spectra for spectral matching. The spectral signatures of all the pixels in the data cube are compared with the reference spectra and the best matching pixel spectra are classified as belonging to the target material.

Identify the classes of endmember materials present in a hyperspectral image. The endmembers are pure spectral signature that signifies the reflectance characteristics of pixels belonging to a single surface material. The existing endmember extraction or identification algorithms extracts or identifies the pure pixels in a hyperspectral image. However, these techniques do not identify the material name or class to which the endmember spectra belong to. In this example, you will extract the endmember signatures and then, classify or identify the class of an endmember material in the hyperspectral image by using spectral matching.

Classify pixels in a hyperspectral image by using the spectral angle mapper (SAM) classification algorithm. This algorithm classifies each pixel in the test data by computing the spectral match score between the spectra of a pixel and the pure spectral signatures read from the ECOSTRESS spectral library. This example uses a data sample from the Jasper Ridge dataset as the test data. The test data contains four endmembers latent, consisting of roads, soil, water, and trees. In this example, you will:

Identify different regions in a hyperspectral image by performing maximum abundance classification (MAC). An abundance map characterizes the distribution of an endmember across a hyperspectral image. Each pixel in the image is either a pure pixel or a mixed pixel. The set of abundance values obtained for each pixel represents the percentage of each endmembers present in that pixel. In this example, you will classify the pixels in a hyperspectral image by finding the maximum abundance value for each pixel and assigning it to the associated endmember class.