A Novel Approach to Super Resolution Mapping of Multispectral Imagery Based on Pixel Swapping Technique

Abstract

ABSTRACT:Mixed pixels could be considered as a major source of uncertainty through classification process of satellite imagery. In this regard, the use of soft classifiers is often inevitable in order to increase the accuracy of land cover estimates. Although soft classifiers provide detailed information for each pixel, spatial arrangement of sub-pixels remains unknown. Super Resolution Mapping (SRM) has opened up a new horizon to produce finer spatial resolution maps using the outputs of soft classifiers. Wide variety of SRM algorithms has been developed. In this way, spatial optimisation techniques are the most applicable ones. However, random allocation of sub-pixels and iterative procedure of optimisation are the main limitations of current methods (e.g. Hopfield Neural Network, Simulated Annealing). This research attempts to provide an optimisation approach based on the pixel swapping technique in order to simplify the concept and to reduce the iteration procedure. In this paper, a brief survey is conducted on spatial optimisation based techniques of SRM. SVM and SMACC are used cooperatively to produce fractional maps as an input of SRM algorithm. The initial allocation of the sub-pixels is performed nonrandomised based on the highest amounts of attractiveness. An optimisation procedure is proposed to transfer the multiple allocated sub-pixels to the non-allocated ones. This procedure usually stops with minimal iterations and is time effective. The proposed method is tested on multispectral imagery (Landsat ETM+ and Quickbird) and has demonstrated precise results particularly in boundary pixels.

Super‐resolution mapping of hyperspectral satellite images using hybrid genetic algorithm

“…This will result in an output (map) with a higher spatial resolution than the one attained by other classification approaches. Markov random field [16–18], pixel swapping algorithm [19–21], particle swarm optimisation [22], linear optimisation technique [23], genetic algorithm (GA) [24], HNN [25, 26] etc. are some of the super‐resolution mapping approaches which take the outputs from the sub‐pixel classification algorithms.…”

Sub Pixel Classification of High Resolution Satellite Imagery

“…According to Niroumand et al (2012) the classification of remote sensing images has always been typical due to frequent occurrence of mixed pixel. At this instant, soft classifiers are often inevitable to achieve good accuracy in classification process.…”