Author:
Firsov N., ,Podlipnov V.,Ivliev N.,Nikolaev P.,Mashkov S.,Ishkin P.,Skidanov R.,Nikonorov A., , , , , , , , , ,
Abstract
In this paper, we propose an approach to the classification of high-resolution hyperspectral images in the applied problem of identification of vegetation types. A modified spectral-spatial convolutional neural network with compensation for illumination variations is used as a classifier. For generating a training dataset, an algorithm based on an adaptive vegetation index is proposed. The effectiveness of the proposed approach is shown on the basis of survey data of agricultural lands obtained from a compact hyperspectral camera developed in-house.
Funder
Russian Science Foundation
Russian Foundation for Basic Research
Publisher
Samara National Research University
Subject
Electrical and Electronic Engineering,Computer Science Applications,Atomic and Molecular Physics, and Optics
Cited by
11 articles.
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