Super-Resolution Mapping of Forests With Bitemporal Different Spatial Resolution Images Based on the Spatial-Temporal Markov Random Field

Abstract

High deforestation rates necessitate satellite images for the timely updating of forest maps. Coarse spatial resolution remotely sensed images have wide swath and high temporal resolution. However, the mixed pixel problem lowers the mapping accuracy and hampers the application of these images. The development of remote sensing technology has enabled the storage of a great amount of medium spatial resolution images that recorded the historical conditions of the earth. The combination of timely updated coarse spatial resolution images and previous medium spatial resolution images is a promising technique for mapping forests in large areas with instant updating at low expense. Super-resolution mapping (SRM) is a method for mapping land cover classes with a finer spatial resolution than the input coarse resolution image. This method can reduce the mixed pixel problem of coarse spatial resolution images to a certain extent. In this paper, a novel spatial-temporal SRM based on a Markov random field, called STMRF_SRM, is proposed using a current coarse spatial resolution Moderate-Resolution Imaging Spectroradiometer image and a previous medium spatial resolution Landsat Thematic Mapper image as input. The proposed model encourages the spatial smoothing of land cover classes for spatially neighboring subpixels and keeps temporal links between temporally neighboring subpixels in bitemporal images. Results show that the proposed STMRF_SRM model can generate forest maps with higher overall accuracy and kappa value.