GRASSLAND RAT-HOLE RECOGNITION AND CLASSIFICATION BASED ON ATTENTION METHOD AND UNMANNED AERIAL VEHICLE HYPERSPECTRAL REMOTE SENSING

IF 0.6 Q4 AGRICULTURAL ENGINEERING

INMATEH-Agricultural Engineering Pub Date : 2023-08-17 DOI:10.35633/inmateh-70-17

Xiangbing Zhu, Yuge Bi, J. Du, Xinchao Gao, Eerdumutu Jin, Fei Hao

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引用次数: 0

Abstract

Rat-hole area and number of rat holes are indicators of the level of degradation and rat damage in grassland environments. However, rat-hole monitoring has consistently relied on manual ground surveys, leading to extremely low efficiency and accuracy. In this paper, a convolutional block attention module (CBAM) model suitable for rat-hole recognition in desert grassland monitoring, called grassland monitoring-CBAM, is proposed that comprehensively incorporates unmanned aerial vehicle hyperspectral remote-sensing technology and deep-learning methods. Validation results show that the overall accuracy and Kappa coefficient of the model were 99.35% and 98.90%, which were 3.96% and 3.35% higher, respectively, than those of the basic model. This study represents a breakthrough in the intelligent interpretation of rat holes and provides technical support for the subsequent rapid interpretation of grassland rat holes and rat damage evaluation. It also provides a solution for the fine classification and quantitative inversion of similar landscape features.

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基于关注法和无人机高光谱遥感的草原鼠洞识别与分类

鼠洞面积和鼠洞数量是草地环境退化程度和鼠害程度的指标。然而，鼠洞监测一直依赖于人工地面调查，导致效率和准确性极低。本文综合运用无人机高光谱遥感技术和深度学习方法，提出了一种适用于沙漠草原监测中鼠洞识别的卷积块注意力模块（CBAM）模型，称为草原监测CBAM。验证结果表明，该模型的总体准确率和Kappa系数分别为99.35%和98.90%，分别比基本模型高3.96%和3.35%。该研究代表了鼠洞智能解读的突破，为后续草原鼠洞快速解读和鼠害评估提供了技术支持。它还为相似景观特征的精细分类和定量反演提供了解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

INMATEH-Agricultural Engineering AGRICULTURAL ENGINEERING-

CiteScore

1.30

自引率

57.10%

发文量