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Neural network-aided classification of hyperspectral vegetation images with a training sample generated using an adaptive vegetation index
Summary
This paper presents a machine learning approach using neural networks to classify plant types in high-resolution hyperspectral aerial images of agricultural fields. The method could be applied to environmental monitoring, including detecting plastic contamination or pollution-induced vegetation changes in farmland.
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.
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