基于人工神经网络和高光谱数据的高山植物群落分类

Bogdan Zagajewski
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引用次数: 2

摘要

本文介绍了塔特拉国家公园(波兰喀尔巴阡山脉南部)高山和亚高山地区植物群落的测绘结果。基于DAIS 7915高光谱图像和2个关键多边形(Biesnik和Uchrocie Kasprowe)的模糊ARTMAP (FAM)神经网络模拟器的分类算法,使用40个原始波段(经过几何和大气校正)和20个MNF波段(来自60个预选DAIS 7915通道)的训练集。将37个植物群落的结果与地面验证获得的参考集进行了比较。使用40个原始波段,测试集的总体精度达到了最佳(87%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of high-mountain plant communities using artificial neural nets and hyperspectral data
The paper presents results of plant communities mapping of an alpine and subalpine zones of the Tatra National Park (southern part of the Polish Carpathian Mts.), located within a range of altitudes of 1500–2549 m a. s. l. Classification algorithm based on the hyperspectral DAIS 7915 imagery and the fuzzy ARTMAP (FAM) neural networks simulator of 2 key polygons (Biesnik and Uchrocie Kasprowe) using training sets of 40 original bands (after geometric and atmospheric correction) and 20 MNF bands (derived from 60 preselected DAIS 7915 channels). The results of 37 plant communities were compared with the reference sets acquired from ground validation. The best overall accuracy (87%) for the test set was achieved using 40 original bands bands.
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