联合多时相Sentinel-2和DeepLabV3+的县域柑橘信息提取
Combined Multi-Temporal Sentinel-2 and DeepLabV3+for County Citrus Information Extraction
玉林海 1韦凯耀 1窦世卿 1丁柏瀚 1韩冰1
作者信息
- 1. 桂林理工大学测绘地理信息学院,桂林 541000
- 折叠
摘要
针对传统分类方法识别柑橘种植空间信息准确率不高的问题,文章提出一种联合多时相Sentinel-2 与DeepLabV3+的县域柑橘种植空间信息提取方法.首先采用优化后的轻量化网络MobileNetV2作为DeepLabV3+的骨干网络,并嵌入CBAM(Convolutional Block Attention Module)注意力机制模块构建改进DeepLabV3+模型;然后利用多时相Sentinel-2 影像整合原始波段与光谱指数构建特征数据集,并通过实验对比分析确定模型分类的最佳特征组合与时相;最后将研究区影像分割为具有重叠度的待预测影像集,结合最优分类模型进行预测后拼接得到柑橘园地提取结果.结果表明:1)改进DeepLabV3+提取精度均高于DeepLabV3+、随机森林模型,在B2～B8A波段添加红边指数RESI的特征组合中总体精度值可达 91.1%,最佳提取时相为 11 月;2)改进DeepLabV3+结合重叠预测方法提取效果优于直接预测方法,且全实验数据集区的提取面积与统计数据的相对误差均保持在±0.04%以内.文中所提出的方法可为南方地区县域范围的柑橘园地自动化监测与种植规划提供参考.
Abstract
Aiming at the problem of low accuracy of traditional classification methods in identifying citrus planting spatial information,this paper proposes a county-level citrus planting spatial information extraction method combining multi-temporal Sentinel-2 and DeepLabV3+.Firstly,the optimized lightweight network MobileNetV2 is used as the backbone network,and the CBAM(Convolutional Block Attention Module)attention mechanism module is embedded to construct the improved lightweight DeepLabV3+;Then,the multi-temporal Sentinel-2 images are used to integrate the original band and spectral index to form the feature data set,and the optimal feature combination and phase of the model classification are determined through experimental comparative analysis;Finally,the image of the study area is segmented into a set of images to be predicted with overlap,and the optimal classification model is used to predict and splice to obtain the results of citrus orchard extraction.The results show that:1)the extraction accuracy of the improved lightweight DeepLabV3+is higher than that of DeepLabV3+and Random Forest model.In the feature combination with the addition of red edge index RESI in the B2-B8A band,the OA can up to 91.1%,and the optimal extraction phase is November.2)The extraction effect of the overlap prediction method is better than that of the direct prediction method,the edge error of the extracted citrus orchard map spots is basically eliminated,and the relative error between the extracted area and the statistical data of the whole region is kept within±0.04%,which is of high applicability.This method can provide a reference for the automatic monitoring and planting planning of citrus orchards in the county area in southern China.
关键词
柑橘/植被指数/多时相Sentinel-2影像/DeepLabV3+/MobileNetV2/注意力机制Key words
citrus/vegetation index/multi-temporal Sentinel-2 images/DeepLabV3+/MobileNetV2/attention module引用本文复制引用
基金项目
国家自然科学基金项目(42161028)
广西八桂学者专项项目(DT2100001072)
桂林市科技局开发项目(2020010701)
桂林市科技局开发项目(20210226-2)
出版年
2024
NETL
NSTL
万方数据