Spatially Adaptive Superresolution Land Cover Mapping With Multispectral and Panchromatic Images

Abstract

Superresolution land cover mapping (SRM) is a technique for generating land cover maps with a finer spatial resolution than the input image. In general, either the original multispectral (MS) images or the spectral unmixing results of the MS image are used as input for SRM models. Panchromatic (PAN) images are often captured together with MS images by many remote sensors and provide more spatial information due to their higher spatial resolution compared with the MS image. In this paper, a spatially adaptive spatial–spectral managed SRM model (SA_SSMSRM) that incorporates both MS and PAN images is proposed. SA_SSMSRM aims to better smooth homogeneous regions of objects (which represent a territory within which there is a uniformity in terms of land cover class) and preserve land cover class boundaries simultaneously by using the PAN image pixel photometric distance (i.e., gray-level distance or pixel value difference). Homogeneous regions in the PAN images are usually characterized by the photometric (pixel value) similarity, whereas class boundaries are usually characterized by photometric dissimilarity. The SA_SSMSRM smoothing parameter, which controls the contribution of the prior term (which encodes prior knowledge about land cover spatial patterns), is designed to be spatially adaptive, with its value decreasing if the photometric similarity of neighboring PAN image pixels decreases. SA_SSMSRM was examined on high-spatial-resolution QuickBird images, IKONOS images, and Advanced Land Observing Satellite (ALOS) images with both MS and PAN data. Results showed that the proposed SA_SSMSRM can generate more accurate superresolution maps than other SRM models.