一种基于多时相植被变化指数图的农作物识别方法
By using multi-temporal vegetation change index maps and random forest models, the problem of inaccurate planting area statistics in remote sensing crop identification was solved, achieving accurate and rapid crop distribution identification and improving data processing efficiency.
CN116858829BActive Publication Date: 2026-07-17ZHEJIANG LINGJIAN SHUZHI TECH CO LTD +1
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG LINGJIAN SHUZHI TECH CO LTD
- Filing Date
- 2023-06-01
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing remote sensing crop identification technologies are prone to confusion when dealing with diverse crop planting conditions, resulting in inaccurate statistics on planting area and distribution, and complex and costly data processing.
Method used
By employing multi-temporal vegetation change index maps, calculating the NDVI change index and phase difference index, and combining them with a random forest identification model, the distribution range of target crops can be identified and statistically analyzed, reducing data processing pressure.
Benefits of technology
It enables accurate identification and statistical analysis of the distribution range of target crops, reduces data processing complexity and cost, and improves identification efficiency.
✦ Generated by Eureka AI based on patent content.
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Figure CN116858829B_ABST
Abstract
本发明公开了一种基于多时相植被变化指数图的农作物识别方法，属于农业遥感技术领域。一种基于多时相植被变化指数图的农作物识别方法，包括：得到待识别区域内的至少一个目标作物的一个作物季的影像数据；得到目标作物在一个作物季内的时相NDVI变化指数，NDVI变化指数表示前后两个相邻时相的影像NDVI的变化速率；训练随机森林识别模型，其输入特征为待识别区域的影像光谱信息、时相NDVI变化指数，其输出特征为所述目标植物的分布范围。它可以实现精准快速地统计并得到目标植被的分布范围和分布面积，并且在识别统计过程中的数据处理压力小。
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