Adopting Gram-Schmidt and Brovey Methods for Estimating Land Use and Land Cover Using Remote Sensing and Satellite Images

Abstract

The production of Land Use and Land Cover thematic maps using remote sensing data is one of the things that must be dealt with carefully to obtain accurate results, data is obtained from sensors of different characteristics. It is not possible to obtain high spatial and spectral accuracy in one image, so we used a fusion image (multispectral image with a low spatial resolution with a panchromatic image with high spatial resolution), which achieved high efficiency in improving the methods of producing Land Use and Land Cover maps. In this study, we used Landsat-8 multispectral and panchromatic images. The study aims to investigate the effectiveness of panchromatic images in improving the methods of producing Land Use and Land Cover maps for the city of Karbala, Iraq. The Support Vector Machine was used to classify the fusion images using the Brovey method and Gram-Schmidt sharpening algorithms. The appropriate methodology for producing Land Use and Land Cover maps was suggested by comparing classifying results and the classification accuracy was evaluated through the confusion matrix. Where the results showed that the method of classifying the fused image by Gram-Schmidt and classified by Support Vector Machine is the best way to produce Land use and Land cover maps for the study area and achieved the highest results for overall accuracy and kappa coefficient of 97.81% and 0.95, respectively.

Fusion Landsat-8 Thermal TIRS and OLI Datasets for Superior Monitoring and Change Detection using Remote Sensing

“…This technique gives a value to the corrected pixel from the nearest uncorrected pixel. However, a disadvantage of applying the neighbour approach is the location errors along with the linear features [33]. In this research, the low spatial resolution image of thermal band 11 has been selected for performing image resampling using the neighbour approach, and the resampling process was conducted using ENVI 5.3 Environment software.…”

Integrating Highly Spatial Satellite Image for 3D Buildings Modelling Using Geospatial Algorithms and Architecture Environment

“…The development of spaceborne sensors has enabled the acquisition of VHR stereo data in a timely and repeatable manner for any region of interest, making it more affordable than traditional aerial surveys. According to [4] [5] [6] [7], the quality of stereoscopic DSMs depends on three factors: 1) The radiometric and geometric quality of the imagery; 2) The accuracy of the sensor model used to represent the relationship between image and object space; 3) The performance of the image matching algorithm. Recent research on extracting 3D information from VHR satellite stereo pairs has mainly focused on Digital-Globe's VHR satellites, such as GeoEye-1 and WorldView-1/2/3/4, which can capture panchromatic imagery of the land surface with a ground sample distance (GSD) even lower than 0.5 m [8].…”

Robust Radiometric Normalization of the Near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis

“…Scenes of the same target area acquired at different times have been nearly impossible to compare without performing image normalization because of variations in atmospheric conditions, look/view angles, or sensor parameters that occur between acquisition time and illumination [26] [27]. Even visual comparison of these images may be difficult [28].…”

“…However, currently, many images can be obtained from practically any region in the world provided for free by the LANDSAT project. Scott et al [28] studied two different scenarios in which three and two images were used, respectively. In the study of Hall [18], a total of six images were used.…”