Sequential Classification of Hyperspectral Images

Abstract

Hyperspectral imaging has become increasingly popular in applications such as agriculture, food, and environment. Rich spectral information of hyperspectral images leads to new possibilities and new challenges in data processing. In this chapter, we consider the hyperspectral classification problems in consideration of sequential data collection, which is a frequent setting in industrial pushboom imaging systems. We present related techniques including data normalization, dimension reduction, classification, and spatial information integration and the way to accommodate these techniques to the context of sequential data collecting and processing. The propose scheme is validated with real data collected in our laboratory. The methodology of result assessment is also presented.

Image recognition model building in the context of municipal waste classification

“…At present, researchers have conducted thorough analysis [2][3] and proposed innovative solutions for the end recycling problem in classification [4][5] . Furthermore, with the rapid development of AI technology, image classification methods based on deep learning have seen a proliferation [6] , resulting in notable achievements in waste recognition [7][8][9][10][11] and image classification [12][13][14] using machine vision technology.…”