A Hybrid Color Mapping Approach to Fusing MODIS and Landsat Images for Forward Prediction

Abstract

We present a simple, and efficient approach to fusing MODIS and Landsat images. It is well known that MODIS images have high temporal resolution and low spatial resolution, whereas Landsat images are just the opposite. Similar to earlier approaches, our goal is to fuse MODIS and Landsat images to yield high spatial and high temporal resolution images. Our approach consists of two steps. First, a mapping is established between two MODIS images, where one is at an earlier time, t 1 , and the other one is at the time of prediction, t p. Second, this mapping is applied to map a known Landsat image at t 1 to generate a predicted Landsat image at t p. Similar to the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SpatioTemporal Image-Fusion Model (STI-FM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) approaches, only one pair of MODIS and Landsat images is needed for prediction. Using seven performance metrics, experiments involving actual Landsat and MODIS images demonstrated that the proposed approach achieves comparable or better fusion performance than that of STARFM, STI-FM, and FSDAF.

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

“…The spatial and temporal adaptive reflectance fusion model (STARFM) has proven to be effective in blending Landsat-MODIS surface reflectance with simulated or real images [41]. Although there are many improved models, such as the spatial-temporal adaptive algorithm for mapping reflectance change (STAARCH) [42], an enhanced STARFM model (ESTARFM) [43], the spatiotemporal integrated temperature fusion model (STITFM) [44], the robust adaptive spatial and temporal fusion model (RASTARFM) [45] and other models [46,47], none of them can generate images with satisfactory spatial and temporal resolutions using only a single pair of images, as the STARFM model does [48][49][50][51][52]. The STARFM algorithm is used in this study to achieve time series Landsat-like data.Many machine-learning algorithms have been used for mapping rice or other land-cover types, such as support vector machines (SVM), random forest (RF), and decision trees (DT) [53][54][55].…”

Fusion of landsat and worldview images

“…Another group focuses on integrating low spatial resolution high temporal resolution images with high spatial resolution low temporal resolution images. See papers [10][11][12][13] and references therein. Some applications include the fusion of MODIS and Landsat [10][11][12] , and the fusion of Planet and Worldview images 13 .…”

“…See papers [10][11][12][13] and references therein. Some applications include the fusion of MODIS and Landsat [10][11][12] , and the fusion of Planet and Worldview images 13 . In the latter group, pansharpening cannot be applied because only low spatial resolution is available at the time of prediction/fusion.…”

“…In this paper, we present some preliminary results on the fusion of Landsat and Worldview images. The proposed algorithm is known as hybrid color mapping (HCM) 11 , which was developed by our team and has been applied to several interesting applications in recent years [14][15][16][17][18][19][20] . The basic idea is to learn a mapping between a pair of Landsat images at t1 and t2 and then the mapping is applied to the Worldview image at t1 to predict the high resolution image at time t2.…”

A New Pansharpening Approach for Hyperspectral Images

“…The second component utilizes the HCM algorithm that fuses a highresolution color image with an enhanced hyperspectral image coming out of the first component. Recently, HCM has been applied to several applications, including enhancing Worldview-3 images [49], fusion of Landsat and MODIS images [50], pansharpening of Mastcam images [51], and fusing of THEMIS and TES [52].…”