Fusion Methods and Multi-classifiers to Improve Land Cover Estimation Using Remote Sensing Analysis

Abstract

Adopting a low spatial resolution remote sensing imagery to get an accurate estimation of Land Use Land Cover is a difficult task to perform. Image fusion plays a big role to map the Land Use Land Cover. Therefore, This study aims to find out a refining method for the Land Use Land Cover estimating using these steps; (1) applying a three pan-sharpening fusion approaches to combine panchromatic imagery that has high spatial resolution with multispectral imagery that has low spatial resolution, (2) employing five pixel-based classifier approaches on multispectral imagery and fused images; artificial neural net, support vector machine, parallelepiped, Mahalanobis distance and spectral angle mapper, (3) make a statistical comparison between image classification results. The Landsat-8 image was adopted for this research. There are twenty Land Use Land Cover thematic maps were generated in this study. A suitable and reliable Land Use Land Cover method was presented based on the most accurate results. The results validation was performed by adopting a confusion matrix method. A comparison made between the images classification results of multispectral imagery and all fused images levels. It proved the Land Use Land Cover map produced by Gram–Schmidt Pan-sharpening and classified by support vector machine method has the most accurate result among all other multispectral imagery and fused images that classified by the other classifiers, it has an overall accuracy about (99.85%) and a kappa coefficient of about (0.98). However, the spectral angle mapper algorithm has the lowest accuracy compared to all other adopted methods, with overall accuracy of 53.41% and the kappa coefficient of about 0.48. The proposed procedure is useful in the industry and academic side for estimating purposes. In addition, it is also a good tool for analysts and researchers, who could interest to extend the technique to employ different datasets and regions.

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

“…Hashim et al (2021) applied the SVM method to low-spatial-resolution Landsat-8 OLI multi-spectral datasets; they got overall accuracy and kappa coefficients of 90.61% and 0.89, respectively [22]. Another study conducted by Dibs et al [34] adopted different fusion methods using a variety of classifiers to classify the satellite images. However, both Hashim et al [22] and Dibs et al [34] studied the same area, but they got less accuracy than the proposed method for this study.…”

“…Images fusion using pan-sharpening algorithms applied to different images that have different resolutions will give the advantage to improve and enhance both quantitative and visual interpretation during image analysis [34]. In this study, the thermal Landsat-8 band (11) was combined and integrated with the visible Landsat-8 image bands; Red, Green, and Blue bands of the OLI sensor, using the GS pan-sharpening technique [35].…”

“…Furthermore, this research is adopting image interpretation with the use of Google Earth for collecting the training and testing that relate to each of the six defined classes. The collected training samples were divided into two categories: the first was employed for conducting image classification with about 70% of the samples, and the second was adopted for conducting the validation of image classification results with about 30% of the samples [34,52]. Finally, the LC maps were generated by using three different classification methods.…”

Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

“…Therefore, the fusion approach based on multiple classifiers that merge into a single map may yield an improved result [38]. Due to its potential to increase accuracy, this technique has been gaining prospects over the last decade and is being widely used for several mapping applications, including land cover mapping [37][38][39][40], wildfire susceptibility mapping [41,42], water edge detection [43] and change detection [44], with limited application in lithological mapping. Notably, the classifier fusion method performed better when the accuracy of each classifier was more than 50%.…”

Dynamic Spatial Monitoring for Urbanization Area Sprawl and Land-Use Using Multi-Spectral and Temporal Remotely Sensed Satellite Images

“…In many cities, urban sprawl has a negative effect on agricultural and vegetation lands, and also on the city's greenery 8 . The urban expansion also will cause landscape transformation, conversion, land degradation, and agricultural lands fragmentation [9][10][11] .…”