Spatio-Temporal Reflectance Fusion Based on 3D Steering Kernel Regression Techniques

Spationtemporal fusion is an effective way to overcome contradictions between high temporal resolution and high spatial resolution of remote sensing, which has a wide range of applications in the city change monitoring, global warming, forest ecology and other environmental issues. STARFM model is a kind of classical and widely used remote sensing Spationtemporal fusion model, but it has two disadvantages. 1) STARFM model uses a fixed-size window to find similar pixels. Because there are both texture-poor areas and texture-abundant areas in an image, the window size should be taken into consideration in Spationtemporal fusion model. 2) STARFM is an isotropic-based algorithm used to determine similar pixels, but images often exhibit heterogeneous isotropic reflectances, especially in the edges of materials. The paper introduces a three-dimensional adaptively local steering kernel regression fusion model (3DSKRFM) to extract local information for each pixel, that is, using the band information of remote sensing data as the third dimension information of the steering kernel, and then using the three-dimensional gradient covariance matrix to obtain the image local geometry information, to achieve its adaptive weight. As a result, it can improve precision of spatiotemporal fusion of remote sensing image. Two datasets associated with ETM+ and MODIS images of Poyang Lake and Fuzhou region are adopted and fusion results of three relational models are compared from the perspective of the quantitative and qualitative in the experiments and the experiments show that 3DSKRFM model not only have the best fusion result, but also have the best ability to deal with noisy image when compared with STARFM and 2DSKRFM models.