Hyperspectral target detection method of binary-classification encoder network based on momentum update

Abstract

A hyperspectral target detection method of a binary-classification encoder network based on a momentum update is provided, and includes following steps: converting an acquired 3-D hyperspectral image into a hyperspectral image in a 2-D matrix form, performing a clustering to obtain a clustering result, and initializing a centroid; based on the clustering result, using Euclidean distance to find pixels adjacent to each centroid as pure background pixels and target pixels, and screening pure pixels; constructing a background-target training sample set based on the pure pixels, constructing a binary-classification encoder network based on a momentum update through the background-target training sample set, calculating a loss function, and optimizing to obtain a trained binary-classification encoder network; inputting the hyperspectral image in the 2-D matrix form into the trained binary-classification encoder network, and outputting a final detection map.

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