深度时空卷积网络支持的地块尺度作物种植类型分类

周亚男; 何金珂; 冯莉; 陈跃红; 吴田军; 张新; 骆剑承

doi:10.13203/j.whugis20230136

深度时空卷积网络支持的地块尺度作物种植类型分类

周亚男¹,
何金珂¹,
冯莉¹,
陈跃红¹,
吴田军²,
张新^3,4, ,,
骆剑承^3,4

1 河海大学水文水资源学院, 江苏南京, 211100;
2 长安大学理学院, 陕西西安, 710064;
3 中国科学院空天信息创新研究院, 北京, 100101;
4 中国科学院大学, 北京, 101408

基金项目:

第三次新疆综合科学考察(2021xjkk1305)；国家重点研发计划(2021YFB3901301)；国家自然科学基金(42071316)。

详细信息

作者简介:
周亚男,博士,副教授,研究方向为高分辨率遥感与时序遥感。zhouyn@hhu.edu.cn

通讯作者:
张新,博士,研究员。zhangxin@radi.ac.cn

中图分类号: P237
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- 文章访问数: 544
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出版历程
- 收稿日期: 2023-04-14
- 网络出版日期: 2023-05-08

Parcel-Scale Crop Type Classification Using Tile-Slice-Based SpatialTemporal Convolutional Networks

1 Collage of Hydrology and Water Resources, Hohai University, Nanjing 211100, China;
2 School of Sciences, Chang'an University, Xi'an 710064, China;
3 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China;
4 University of Chinese Academy of Sciences, Beijing 101408, China

摘要

- Sentinel-2遥感影像 /
- 地块尺度 /
- 作物类型 /
- 时空卷积网络 /
- 分区分层
Abstract:
Objectives: Parcel-based crop classification using multi-temporal satellite images plays a vital role in precision agriculture. However, exploring the multi-scale spatial information for identification of crop types from remote sensing images is a significant challenge. Methods: In this study, a tile-slice-based spatial-temporal method was developed for parcel-scale crop (type) classification using multi-temporal Sentinel-2 images. Central to this approach is the combined use of tile-slice-based representation of parcels in images and a deep spatial-temporal convolutional network. Firstly, spatial tiles were sliced according to crop types and parcels, to produce a tile-slicebased training sample dataset and a tile-slice-based prediction dataset. Then, the deep spatial-temporal convolutional network was established to estimate crop-type probabilities for tile-slice-based prediction dataset. Finally, tile-slicebased probabilities were fused under parcel polygons, to generate the final crop type maps. Results: The proposed method is further discussed and validated through parcel-based time-series crop classifications in the France study area with multi-temporal Sentinel-2 images. The classification results demonstrated great improvements in accuracy scores (0.03, 0.02, 0.02 in overall accuracy, precision and F1, respectively) over comparison methods. Conclusions: Through experiments and discussions, we concluded that: (1) the proposed tile-slice-based spatial-temporal method is effective for parcel-based crop classification using Sentinel-2 image sequences, (2) the spatial division and slice in parcel-scale representation are benefit more for crop type identification of larger and spatially concentrated parcels, (3) the proposed TSST-Net is lightweight but powerful for crop classification. The above experiments and their conclusions will provide new ideas for parcel-based agricultural remote sensing time series analysis.
- Sentinel-2 image /
- parcel-scale /
- crop type mapping /
- spatial-temporal convolutional networks /
- spatial division and slicing

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