Article Remote Sensing Image Fusion at the Segment Level Using a Spatially-Weighted Approach: Applications for Land Cover Spectral Analysis and Mapping

Abstract: Segment-level image fusion involves segmenting a higher spatial resolution (HSR) image to derive boundaries of land cover objects, and then extracting additional descriptors of image segments (polygons) from a lower spatial resolution (LSR) image. In past research, an unweighted segment-level fusion (USF) approach, which extracts information from a resampled LSR image, resulted in more accurate land cover classification than the use of HSR imagery alone. However, simply fusing the LSR image with segment polygons may lead to significant errors due to the high level of noise in pixels along the segment boundaries (i.e., pixels containing multiple land cover types). To mitigate this, a spatially-weighted segment-level fusion (SWSF) method was proposed for extracting descriptors (mean spectral values) of segments from LSR images. SWSF reduces the weights of LSR pixels located on or near segment boundaries to reduce errors in the fusion process. Compared to the USF approach, SWSF extracted more accurate spectral properties of land cover objects when the ratio of the LSR image resolution to the HSR image resolution was greater than 2:1, and SWSF was also shown to increase classification accuracy. SWSF can be used to fuse any type of imagery at the segment level since it is insensitive to spectral differences between the LSR and HSR images (e.g., different spectral ranges of the images or different image acquisition dates)