Identify Vegetation in a Multispectral Image using MATLAB

Introduction—

MATLABSolutions demonstrate In this task we are going to design The This illustration demonstrates employing MATLAB® array arithmetic for image processing and visualization of image data. Specifically, it operates on a three-dimensional image array, where the three planes denote the image signal captured from distinct segments of the electromagnetic spectrum, encompassing the visible red and near-infrared (NIR) channels.

Image data differences can be used to distinguish different surface features of an image, which have varying reflectivity across different spectral channels. By finding differences between the visible red and NIR channels, the example identifies areas containing significant vegetation.

Step 1: Import Color-Infrared Channels from a Multispectral Image File

In this instance, vegetation detection is performed on a LANDSAT Thematic Mapper image, portraying a segment of Paris, France, generously provided by Space Imaging, LLC. The image is stored in the Erdas LAN format within the file paris.lan, encapsulating seven spectral channels (bands) in a 512-by-512 Landsat image. A 128-byte header precedes the pixel values, arranged in band interleaved by line (BIL) format in ascending band number sequence. Pixel values, represented as unsigned 8-bit integers, adhere to little-endian byte order.

The initial step involves reading bands 4, 3, and 2 from the LAN file using the MATLAB® function multibandread.

Channels 4, 3, and 2 correspond to the near-infrared (NIR), visible red, and visible green regions of the electromagnetic spectrum. When these channels are mapped to the red, green, and blue planes, respectively, in an RGB image, a standard color-infrared (CIR) composite is generated. The final argument in multibandread dictates the bands to read and their order, facilitating the creation of a composite in a single, efficient step.