Comparative Analysis and Classification of Multispectral Remote Sensing Data - Kusum; Nisha (International Journal of Computer Sciences and Engineering, 2014)
The objective of this paper is to utilize the features obtained by the artificial neural network rather than the original multispectral features of remote-sensing images for land cover classification. WT provides the spatial and spectral characteristics of a pixel along with its neighbors and hence, this can be utilized for an improved classification. And the combination of remote sensing and geographic ancillary data is believed to offer improved accuracy in land cover classification. This paper focuses on the Image Analysis of Remote Sensing Data Integrating Spectral and Spatial Features of Objects in the area of satellite image processing. Here multi-spectral remote sensing data is used to find the spectral signature of different objects.