Review of Classification Methods for Crop Structure in Remote Sensing Imagery

doi:10.3778/j.issn.1002-8331.2409-0362

Abstract

Abstract: Remote sensing imagery plays a significant role in the classification of crop planting structures. This paper reviews the primary classification techniques in remote sensing imagery, including spectral features, texture features, temporal features, and multi-source data fusion. It focuses on analyzing the performance of traditional classification methods, as well as deep learning techniques like convolutional neural networks in improving classification accuracy and efficiency. The research indicates that integrating multi-source remote sensing data with deep learning models substantially enhances crop classification outcomes, especially in complex environments and when processing multi-temporal data. In the future, advancements in algorithm optimization and data fusion in remote sensing imagery classification will further drive the development of precision agriculture and the construction of intelligent management systems.

Key words: remote sensing technology, crop classification, deep learning, data fusion

摘要： 遥感影像中的农作物种植结构分类具有重要应用价值。综述了遥感影像的主要分类技术，包括光谱特征、纹理特征、时序特征和多源数据融合等方法；重点分析了传统分类方法以及卷积神经网络等深度学习技术在提升分类精度和效率方面的表现。研究结果表明，结合多源遥感数据与深度学习模型显著提高了复杂环境下的作物分类效果，尤其在处理多时相数据时表现突出。未来，遥感影像分类将通过算法优化和数据融合，进一步推动精准农业的发展与智能化管理系统的构建。

关键词: 遥感技术, 农作物分类, 深度学习, 数据融合

ZHEN Tong, ZHANG Weizhen, LI Zhihui. Review of Classification Methods for Crop Structure in Remote Sensing Imagery[J]. Computer Engineering and Applications, 2025, 61(8): 35-48.

甄彤, 张威振, 李智慧. 遥感影像中种植作物结构分类方法综述[J]. 计算机工程与应用, 2025, 61(8): 35-48.

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