地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (8): 1702-1713.doi: 10.12082/dqxxkx.2020.200086
王学文1,2(), 赵庆展1,2,*(
), 韩峰1,2, 马永建1,2, 龙翔2,3, 江萍2,3
收稿日期:
2020-02-23
修回日期:
2020-03-25
出版日期:
2020-08-25
发布日期:
2020-10-25
通讯作者:
赵庆展
E-mail:wangxuewen@stu.shzu.edu.cn;zqz_inf@shzu.edu.cn
作者简介:
王学文(1996— ),男,江苏南通人,硕士生,主要从事农业信息化技术及应用、深度学习与遥感影像处理研究。 E-mail:基金资助:
WANG Xuewen1,2(), ZHAO Qingzhan1,2,*(
), HAN Feng1,2, MA Yongjian1,2, LONG Xiang2,3, JIANG Ping2,3
Received:
2020-02-23
Revised:
2020-03-25
Online:
2020-08-25
Published:
2020-10-25
Contact:
ZHAO Qingzhan
E-mail:wangxuewen@stu.shzu.edu.cn;zqz_inf@shzu.edu.cn
Supported by:
摘要:
农田防护林是农田生态系统的屏障,其健康状况的监测与评估在我国北方农田林网管理中尤为重要。本文以新疆生产建设兵团第三师51团为研究区,使用复合翼无人机CW-20搭载Micro MCA12 Snap多光谱相机获取农田防护林的多光谱影像,经辐射校正、裁剪等预处理,通过优选有效特征和模型比较,提出农田防护林提取的有效方法。首先,基于原始12波段,依据相关性系数矩阵和最佳指数因子(Optimum Index Factor,OIF)选取最优3波段和植被指数特征进行组合,构建8种农田防护林提取方案;然后,通过建立语义分割Deeplabv3+模型进行精度评价,得到最优3波段组合6(波长710 nm)、8(波长800 nm)、 11(波长900 nm)波段为最佳特征组合;最后,以最优3波段为基础,将Deeplabv3+模型与U-Net、ENVINet5模型进行对比分析。结果表明:Deeplabv3+模型能够更深层次的挖掘光谱中潜在的信息,相比其他模型,能够较好地处理正负样本不均衡问题,获得最高MIoU值85.54%,比U-Net、ENVINet5的MIoU值则分别高出21.21%、27.19%。该研究结果可为基于多光谱遥感影像的语义分割在农田防护林提取及健康状况监测的应用提供借鉴和参考。
王学文, 赵庆展, 韩峰, 马永建, 龙翔, 江萍. 机载多光谱影像语义分割模型在农田防护林提取中的应用[J]. 地球信息科学学报, 2020, 22(8): 1702-1713.DOI:10.12082/dqxxkx.2020.200086
WANG Xuewen, ZHAO Qingzhan, HAN Feng, MA Yongjian, LONG Xiang, JIANG Ping. Application of Airborne Multispectral Image Semantic Segmentation Model in Farmland Shelterbelt Extraction[J]. Journal of Geo-information Science, 2020, 22(8): 1702-1713.DOI:10.12082/dqxxkx.2020.200086
表2
植被指数及计算公式"
指数 | 中文名称 | 英文名称 | 对应Micro MCA12 Snap波段 | 计算公式及编号 | |
---|---|---|---|---|---|
NDVI[ | 归一化植被指数 | Normalized difference vegetation index | B8、B5 | (3) | |
NDVIre[ | 红边归一化差值植被指数 | Red-edge normalized difference vegetation index | B8、B7、B8、B6 | (4) | |
MTCI[ | 地面叶绿素指数 | MERIS Terrestrial Chlorophyll Index | B7、B6、B5 | (5) | |
NDRE[ | 归一化差值红边指数 | Normalized difference Red Edge index | B7、B6 | (6) |
表4
研究区域12波段相关性系数矩阵"
波段 | 波段 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |
1 | 1.000 | |||||||||||
2 | 0.982 | 1.000 | ||||||||||
3 | 0.960 | 0.966 | 1.000 | |||||||||
4 | 0.968 | 0.973 | 0.963 | 1.000 | ||||||||
5 | 0.958 | 0.962 | 0.940 | 0.991 | 1.000 | |||||||
6 | 0.827 | 0.850 | 0.922 | 0.862 | 0.831 | 1.000 | ||||||
7 | -0.330 | -0.302 | -0.192 | -0.357 | -0.395 | 0.087 | 1.000 | |||||
8 | -0.330 | -0.300 | -0.188 | -0.357 | -0.398 | 0.099 | 0.966 | 1.000 | ||||
9 | -0.345 | -0.315 | -0.204 | -0.372 | -0.410 | 0.081 | 0.964 | 0.982 | 1.000 | |||
10 | -0.339 | -0.309 | -0.198 | -0.364 | -0.402 | 0.089 | 0.967 | 0.981 | 0.971 | 1.000 | ||
11 | -0.335 | -0.308 | -0.199 | -0.362 | -0.397 | 0.080 | 0.965 | 0.952 | 0.947 | 0.979 | 1.000 | |
12 | -0.261 | -0.239 | -0.119 | -0.281 | -0.319 | 0.169 | 0.931 | 0.943 | 0.944 | 0.960 | 0.957 | 1.000 |
表6
Deeplabv3+语义分割精度评价"
地物类型 | 指标 | 方案1 | 方案2 | 方案3 | 方案4 | 方案5 | 方案6 | 方案7 | 方案8 |
---|---|---|---|---|---|---|---|---|---|
农田防护林 | F1值 | 0.90 | 0.92 | 0.88 | 0.91 | 0.79 | 0.90 | 0.71 | 0.76 |
查准率precision/% | 84.83 | 88.40 | 81.94 | 86.28 | 67.57 | 86.06 | 39.13 | 62.29 | |
查全率recall/% | 95.09 | 95.42 | 95.49 | 95.62 | 95.57 | 94.84 | 94.73 | 96.10 | |
IoU/% | 73.66 | 79.21 | 69.41 | 75.87 | 51.02 | 75.53 | 24.32 | 45.23 | |
其他地物 | F1值 | 0.91 | 0.92 | 0.90 | 0.91 | 0.85 | 0.91 | 0.81 | 0.83 |
查准率precision/% | 95.62 | 95.76 | 96.13 | 96.05 | 96.87 | 95.32 | 98.44 | 97.46 | |
查全率recall/% | 86.31 | 89.20 | 84.84 | 87.50 | 74.92 | 87.24 | 69.30 | 72.10 | |
IoU/% | 91.61 | 91.61 | 91.86 | 92.40 | 92.14 | 91.06 | 96.93 | 94.13 | |
平均 | MIoU/% | 82.63 | 85.54 | 80.98 | 84.14 | 72.48 | 83.29 | 60.63 | 69.75 |
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