抗干扰的农作物种植模式自动提取方法

项目名称： 抗干扰的农作物种植模式自动提取方法

项目编号： No.41471362

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 天文学、地球科学

项目作者： 邱炳文

作者单位： 福州大学

项目金额： 88万元

中文摘要： 农作物种植模式的遥感自动提取对于农业信息更新并确保粮食安全具有重要意义。消除植被指数时序数据噪声与类内变化的干扰，是农作物种植模式遥感监测所面临的关键技术问题。本项目拟提出一种能抗干扰的农作物种植模式自动提取方法：首先基于多层次自适应时空建模框架，探索植被演变的时空过程与驱动机制，开展植被指数时序信号分析与重构，以合理消除数据噪声，并科学评估与量化类内变化的干扰特征；进而借鉴模式识别领域的技术方法，综合植被与气候因子的年内、年际变化等多维度信息，通过提取其时频图谱-特征图谱-相关性图谱的鲁棒性特征，以有效避免类内变化干扰，最终创建农作物种植模式自动提取方法。在算法研究的基础上，实现对中国主要农业区农作物种植模式的自动精确提取，并揭示历年耕地复种指数时空演变规律。该方法可拓展用于其他植被覆盖变化遥感监测，促进时序分类技术的发展，并有望为解决遥感分类异物同谱与同物异谱问题开拓新思路。

中文关键词： 耕地监测；农作物种植模式；抗干扰；特征提取；时序分析

英文摘要： It is very significant for the agricultural information updating and food security to extract agricultural cropping pattern from remote sensing images automatically. For research on remote sensing monitoring of agricultural cropping patterns, there are two important issues needed be addressed: one issue is the data quality, which was generally associated with noise; another issue is the intra-class variability of vegetation indices (VI) temporal profile. This project aims to develop an anti-disturbance automatic method for deriving agricultural cropping patterns. First, the spatiotemporal process and its driving mechanism of vegetation dynamic are investigated, and original VI time series datasets are reconstructed for the purpose of eliminating noise under the multi-level adaptive spatiotemporal modeling framework. During this process, the characteristic of intra-class variability of vegetation indices (VI) temporal profile are evaluated. Second, with the reference of pattern identification methods, characteristics of robustness will be derived through the technical thinking from original profile, frequency-temporal profile to characteristic profile and correlation profile, with consideration of intra- and inter-annual variability of vegetation and climate indices. Finally, an anti-disturbance automatic method for deriving agricultural cropping patterns will be developed. The datasets of agricultural cropping pattern will be derived automatically through its application in main cropping areas of China, and then the spatiotemporal dynamic of cropping index in main cropping areas of China over the years will also be investigated. This method can easily be adapted to other vegetation dynamic monitoring based on remote sensing time series datasets. It is hoped that the outcomes of this project will greatly improve the technical level of time-series classification, and also provide new thinking for solving the problem of the same object with different spectrums and different spectra with the same spectrum.

英文关键词： farmland monitoring;agricultural cropping pattern;anti-disturbance;feature selection;time series analysis