| Currently, remote sensing imagery has been widely used for generating global land cover products, but due to certain physical and budget limitations related to the sensors, their spatial and temporal resolution are too low to attain more accurate and more reliable global change research. In this situation, there is an urgent need to study and develop a more advanced satellite image processing method and land cover producing techniques to generate higher resolution images and land cover products for global change research.;Through conducting a comprehensive study of the related theories and methods related to data fusion, various methods are systematically reviewed and summarized, such as HIS transformation image fusion, Wavelet transform image fusion, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), etc. The advantages and disadvantages of these methods are highlighted according to their specific applications in the field of remote sensing. Based on my research target, the following are the main contents of this thesis:;(1) Data fusion theory will be systematically studied and summarized, including various fusion models and specific applications, such as IHS transformation, PCA transformation, Wavelet analysis based data fusion, etc. Furthermore, their advantages and disadvantages are pointed out in relation to specific applications.;(2) As traditional data fusion methods rely on spatial information and it is hard to deal with multi-source data fusion with temporal variation, therefore, the traditional data fusion theory and methods will be improved by a consideration of temporal information. Accordingly, some spatial and temporal data fusion methods will be proposed, in which both high-resolution & low-temporary imagery and low-resolution & high-temporary imagery are incorporated. Our experiments also show that they are suitable for dealing with multi-temporal data integration and generating high-resolution, multi-temporal images for global change research.;(3) There are two main issues related to spatial and temporal data fusion theory. The first is that there are inconsistencies in different images, such as the different levels of land surface reflectance and different degrees of reliability of multi-source satellite data. The second is the rule of phonological variation/land cover variation in both the spatial and temporal dimensions, particularly in areas with heterogeneous landscapes. When considering these issues, an improved STARFM (spatial and temporal adaptive reflectance fusion model) is proposed, based on the original model, and the preliminary results show that it is more efficient and accurate in generating high-resolution land surface imagery than its predecessor.;(4) Mixed pixels is a common issue in relation to satellite data processing, as one pixel in a coarse resolution image will constitute several pixels in a high-resolution image of the same size, so different levels of land surface reflectance will be acquired from multi-source satellite data because of the mixed pixel effect on the coarse resolution data, and the final accuracy of the fused result will be affected if these data are subjected to data fusion. In order to solve the mixed pixel issue in multi-source data fusion, an improved spatial and temporal data fusion approach, based on the constraint unmixing technique, was developed in this thesis. The experimental results show that it is well-suited to the phenological monitoring task when a prior land cover map is available.;(5) Based on the high-resolution reflectance images generated from spatial and temporal fusion, a spatial and temporal classification method based on the spatial and temporal Markov random field was developed to produce a high-resolution land cover product, in which both spatial and temporal contextual information are included within the classification scheme. This method provides a new strategy for generating high-resolution land cover products in the area without high-resolution images at a certain time, and the experimental results show that it is acceptable and suitable for generating high quality land cover products in areas for which there is a lack of high-resolution data. (Abstract shortened by UMI.). |
