Extraction of Rape Planting Information in Cultivated Land Fragmentation Region Based on HJ‑2 CCD Imagery

doi:10.15933/j.cnki.1004-3268.2025.07.018

Abstract

Abstract: Owing to the diverse landscape pattern of farmland，complex crop planting structure，and scattered as well as irregular plots in the cultivated land fragmentation region，it is prone to have a severe mixed pixel phenomenon when applying medium and low spatial resolution remote sensing imagery，which makes it difficult to obtain high‑precision crop planting information. The popularization and use of high spatial resolution remote sensing imagery has improved this situation and increased the accuracy of crop identification in cultivated land fragmentation region，but the research on planting extraction based on high spatial resolution remote sensing imagery for rape is still relatively scarce at present，and the existing identification methods suffer from the problems of large amount of data processing and complicated process. To address this，based on the HJ‑2 CCD imagery with both spatial resolution and spectral band enhancement in Zongyang County，Anhui Province，a rapid and effective rape identification model for fragmented regions of cultivated land was proposed by utilizing the derived spectral feature set constructed by vegetation indices of key phenological stage image of rape blossoming as well as combining the Bagging ensemble strategy and minimum Mahalanobis distance classifier. The results of the study showed that，compared with five other typical rape identification modeling methods，RAW+SVM，RAW+RF，RAW+Bagging‑MMDC，VI+SVM，VI+RF，the proposed model VI+Bagging‑MMDC had the best results in land cover classification and rape extraction，with overall accuracy，Kappa coefficient，user accuracy of rape，and producer accuracy of rape being 94.27%，0.93，98.59% and 97.21%，respectively；Compared with the statistical data，the total area accuracy of the proposed model reached 92.80%，the relative error was 7.20%，and the planting distribution was highly consistent with the actual rape plots，thereby meeting the practical application requirements. The introduction of red‑edge information increased the overall accuracy，Kappa coefficient and user accuracy of rape by 2.40 percentage points，0.03 and 4.94 percentage points，respectively. This study proves that the combined use of key vegetation index of rape blossoming stage，Bagging ensemble strategy，minimum Mahalanobis distance classifier and red‑edge information can effectively improve the extraction accuracy of crops in the cultivated land fragmentation region.

Key words: Rape, Planting extraction, Ensemble learning, Minimum Mahalanobis distance classifier, Vegetation index, Red?edge information, HJ?2 satellite, Cultivated land fragmentation region

摘要： 由于耕地破碎区农田景观格局多样、作物种植结构复杂、地块零散且不规整，使用中低空间分辨率遥感影像容易出现严重的混合像元现象，难以获得高精度的农作物种植信息。高空间分辨率遥感影像的普及使用改善了这种情况，提高了耕地破碎区的农作物识别精度，但目前针对油菜的高空间分辨率遥感影像种植提取研究仍较少，已有识别方法存在数据处理量大、过程复杂的问题。针对此，以安徽省枞阳县为研究区，基于空间分辨率与光谱波段双提升的HJ-2 CCD影像，提出一种利用油菜开花期的关键物候期影像植被指数构建衍生光谱特征集、联合Bagging集成策略和最小马氏距离分类器的快速有效的耕地破碎区油菜识别模型。结果表明，与RAW（原生光谱波段）+SVM（支持向量机）、RAW+RF（随机森林）、RAW+Bagging-MMDC（Bagging集成最小马氏距离分类器）、VI（植被指数特征集）+SVM、VI+RF 5种其他典型模型方法相比，所提出模型VI+Bagging-MMDC的地物分类和油菜提取效果最好，总体精度、Kappa系数、油菜的用户精度、油菜的生产者精度分别为94.27%、0.93、98.59%、97.21%；与统计数据相比，所提出模型的总面积精度达到92.80%，相对误差为7.20%，种植分布与真实油菜地块吻合度高，可以满足实际应用需求；红边信息的引入使总体精度、Kappa系数、油菜的用户精度分别提高2.40百分点、0.03、4.94百分点。油菜开花期关键性植被指数、Bagging集成策略、最小马氏距离分类器以及红边信息的结合使用可以有效提高耕地破碎区作物的提取精度。

关键词: 油菜, 种植信息提取, 集成学习, 最小马氏距离分类器, 植被指数, 红边信息, 环境二号卫星, 耕地破碎区

CLC Number:

S127

ZHANG Meng, XU Xiang, WANG Zhuang, WANG Jie. Extraction of Rape Planting Information in Cultivated Land Fragmentation Region Based on HJ‑2 CCD Imagery[J]. Journal of Henan Agricultural Sciences, 2025, 54(7): 170-180.

张萌, 徐祥, 王状, 王杰. 基于HJ-2 CCD 影像的耕地破碎区油菜种植信息提取[J]. 河南农业科学, 2025, 54(7): 170-180.

Figures/Tables 11

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