Institute of Geographic Sciences and Natural Resources Research , Chinese Academy of Sciences (IGSNRR, CAS)
Super resolution mapping (SRM) technique decomposes each mixed pixel into
a fixed number of subpixels based on a zoom factor and it then assigns these
subpixels to specific LULC classes. In contrast to common classifiers, SRM
can produce a LULC map with finer spatial resolution than the original
moderate- or low-spatial-resolution input image. Thus, SRM offers a solution
to the tradeoff between the spatial resolution of a sensor and its spectrum.
It can process mixed pixels in low-, moderate-, and high-spatial-resolution
imagery and thus save on the cost of obtaining images with higher spatial
resolution and SRM can be applied to low- or moderate-resolution images
acquired in an earlier phase to produce LULC maps with spatial resolution
consistent with maps produced from high-spatial-resolution images acquired
during the current phase. It is therefore attractive to use SRM to derive
finer-resolution LULC maps from coarser-resolution remote sensing images.
We developed a series of SRM techniques and verified the effectiveness of
the techniques through the experiment. Further, we discussed the applications
of SRM results, for example to investigate the consistency of the classification
results from SRM and higher spatial resolution remote sensing image, the zoom
factor setting for SRM. We developed the corresponding evaluation indexes and
discussed the applicability of the SRM techniques in the real applications.