农作物遥感识别中的多源数据融合研究进展

农作物遥感识别是地理学和生态学研究的前沿和热点,多源数据在农作遥感识别中日益发挥重要作用。笔者从多源数据融合的角度,归纳了2000年后多源数据在农作物遥感识别中应用的总体概况,系统梳理并提炼了当前多源数据融合的主要融合技术和融合模式。围绕与多源数据融合和农作物遥感识别相关的关键词,在Google学术、ISI Web of Knowledge和中国知网中对2000—2014年间国内外发表的论文进行检索,并统计不同传感器的使用频率及结合方式。研究表明,以提高空间分辨率为目标的多源数据融合和以提高时间分辨率为目标的多源数据融合技术是当前的两种主要方式,可以在一定程度上实现时空尺度的扩展。前者的融合技术包括图像融合、正态模糊分布神经网络模型、成分替换、半经验数据模型融合及多分辨率小波分解等,可以提升遥感数据的空间分解力和清晰度,较好弱化混合像元产生的影响,但农作物光谱信息有一定程度的丢失或扭曲,农作物空间分布局部细节信息与纹理特征依然会缺失;后者的融合技术形式灵活多样,可分为同源数据联合扩展时序的时空优化技术和异源数据联合扩展时序的时空优化技术,其可以有效排除短时间段内农作物生育期交叉,但易受不同遥感数据源间光谱反射率或植被指数转换模型及光谱波段设置差异的影响。在融合模式方面,根据数据类型分为光学数据的融合、光学数据与微波数据的融合以及遥感与非遥感数据的融合,以实现卫星资源优势互补为宗旨,充分挖掘不同类型农作物在遥感数据上呈现的光谱、时间和空间特征差异信息。同样,农作物遥感识别研究中的多源遥感数据融合也存在诸多挑战,在未来一段时间内,完善不同传感器之间的合作、更深层次挖掘融合信息以及多尺度长时间序列的中高分辨率农作物空间分布数据集的需求是多源数据融合的农作物遥感识别研究的重点发展方向和亟待解决的问题。研究结果有助于更好地理解多源遥感数据融合的技术和模式,为摸清多源数据融合在农作物识别中总体进展提供支撑,同时也为其他多源数据融合研究提供借鉴。

Recent Progresses in Research of Integrating Multi-Source Remote Sensing Data for Crop Mapping

Crop mapping by using the remotely-sensed images provide basic information for further geographical and ecological researches. A systematic review on the recent literature regarding crop mapping was carried out in order to improve our understanding on the integration and application of multi-source remote sensing data. The literature search was performed in Google Scholar, the ISI Web of Knowledge and CNKI (e.g. Topic =”crop + mapping”; Topic =”classification + multi-source”; timespan = 2000-2014). According to the thorough analysis on the existing publications, it is suggested that (1) there are two main ways to identify crop types based on the integration of multi-source data in order to expand the spatial and temporal scales. The techniques of multi-source data fusion, which are aimed at improving the spatial resolution, include image fusion, normal fuzzy distributed neural networks, component substitution, semi-physical fusion approach, and multiresolution wavelet decomposition. With the integrated application of such approaches, the spatial resolution and clarity of remote sensing images are raised; the effect of mixed pixels is weaken to some extent. Nevertheless, crop spectral information is partly lost or distorted. The techniques of multi-source data fusion, which are aimed at improving the temporal resolution, can be categorized into two types: the integration of the same data source, and the integration of different data sources. By using such approaches, the crossover of growth period among different crops can be effectively eliminated. But such approaches are susceptible to transformation models of spectral reflectance or vegetation indices, and the differences in band coverage among different remote sensing data. (2) The modes of multi-source data fusion can be categorized into three types according to the data types applied: integration of optical data, integration of optical and microwave data, and integration of remote sensing and ancillary data sources. Taking complementary advantages of various satellite data resources, these techniques of data fusion fully mine the differences of spectral, temporal and spatial characteristics, among various crop species. However, there still remain challenges in previous researches about the crop identification based on the fusion of multi-source remotely sensed data.