增强型空间像元分解时空遥感影像融合算法  

An enhanced unmixing model for spatiotemporal image fusion

作　　者：黄波[1,2,3] 姜晓璐 HUANG Bo;JIANG Xiaolu(Institute of Space and Earth Information Science,The Chinese University of Hong Kong,Hong Kong 999077,China;Department of Geography and Resource Management,The Chinese University of Hong Kong,Hong Kong 999077,China;Shenzhen Research Institute,The Chinese University of Hong Kong,Shenzhen 51805,China)

机构地区：[1]香港中文大学太空与地球信息科学研究所,中国香港999077 [2]香港中文大学地理与资源管理学系,中国香港999077 [3]香港中文大学深圳研究院,深圳518005

出　　处：《遥感学报》2021年第1期241-250,共10页NATIONAL REMOTE SENSING BULLETIN

摘　　要：高空间、高时间分辨率的遥感影像对地表与大气环境的实时精细监测具有重要作用,但单一卫星传感器获取的遥感影像存在空间与时间分辨率相互制约的问题,时空融合技术发展成为了低成本、高效生成满足不同应用需求的高时空分辨率遥感影像的有效手段。近年来,国内外学者提出了大量的时空融合算法,但对于复杂的地物类型变化的空间细节修复仍存在挑战,融合影像精度有待提高。对此,本文提出增强型空间像元分解时空遥感影像融合算法(EUSTFM),采用变化检测识别并修复地物类型改变的像元,使空间像元分解过程可同时在已知时相与未知时相进行,以生成空间细节信息准确的中间分辨率影像对,用于最终的邻域相似像元计算,实现了对季节性变化(如植被自然生长)、有形变(如城市土地扩张)及无形变的地物类型变化(如农作物的成熟与收割)等复杂地表变化的一致性预测,提高了融合精度。实验采用两对Landsat-MODIS遥感影像数据集,对比STARFM与FSDAF两种广泛应用的时空融合算法,测试了该算法的影像融合效果。结果表明,本文提出的EUSTFM能够同时实现对季节性变化及复杂的地物类型变化的稳定预测,可生成具有更高精度的融合影像,将有效推动时空影像融合的实际遥感应用。Remote sensing images with high spatial and temporal resolutions are vital for the real-time and fine monitoring of land surface and atmospheric environment. However, a single satellite sensor has to tradeoff between the spatial and temporal resolutions due to technical and budget limitations. In recent years, numerous spatial and temporal image fusion models have been proposed to produce high-resolution images with low cost and remarkable effectiveness. Despite the varying levels of success in the accuracy of fused images and the efficiency of algorithms, challenges always remain on the recovery of spatial details along with the complex land cover changes. This study presented an enhanced unmixing model for spatial and temporal image fusion(EUSTFM) that accounts for phenological changes(e. g., vegetation growth) and shape(e.g., urban expansion) and non-shape land cover changes(e.g., crop rotation) on the land surface simultaneously. First, a change detection method was devised to identify the pixels with land cover change. The similar pixels of the detected pixels were then searched in the neighborhood to recompose the spectral reflectance on the prediction date. Thus, the real land cover class on the prediction date can be defined using the recomposed high-resolution image rather than directly using the classification result from a prior date.Subsequently, the spatial unmixing of pixels can be conducted on the prior and prediction dates to produce a medium-resolution image pair with accurate spatial details. Finally, the calculation of the similar pixels in the neighborhood was implemented for the final prediction of the fused images using all the original high and low-resolution image pair in the prior time, low-resolution image in the prediction time, and the produced medium-resolution image pair in the prior and prediction times. This study tested the algorithms with two actual Landsat-MODIS datasets: one dataset focusing on typical phenological changes in a complex landscape in Australia and the other datas

关 键 词：时空融合 遥感影像 空间像元分解 变化检测 时间分辨率 空间分辨率 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]