Improvement in the Accuracy of the Postclassification of Land Use and Land Cover Using Landsat 8 Data Based on the Majority of Segment-Based Filtering Approach

Fajar Yulianto,Gatot Nugroho,Suwarsono Suwarsono,Galdita Aruba Chulafak,Junsheng Yu

doi:10.1155/2021/6658818

Abstract

Improvement in the accuracy of the postclassification of land use and land cover (LULC) is important to fulfil the need for the rapid mapping of LULC that can describe the changing conditions of phenomena resulting from interactions between humans and the environment. This study proposes the majority of segment-based filtering (MaSegFil) as an approach that can be used for spatial filters of supervised digital classification results. Three digital classification approaches, namely, maximum likelihood (ML), random forest (RF), and the support vector machine (SVM), were applied to test the improvement in the accuracy of LULC postclassification using the MaSegFil approach, based on annual cloud-free Landsat 8 satellite imagery data from 2019. The results of the accuracy assessment for the ML, RF, and SVM classifications before implementing the MaSegFil approach were 73.6%, 77.7%, and 77.5%, respectively. In addition, after using this approach, which was able to reduce pixel noise from the results of the ML, RF, and SVM classifications, there were increases in the accuracy of 81.7%, 85.2%, and 84.3%, respectively. Furthermore, the method that has the best accuracy RF classifier was applied to several national priority watershed locations in Indonesia. The results show that the use of the MaSegFil approach implemented on these watersheds to classify LULC had a variation in overall accuracy ranging from 83.28% to 89.76% and an accuracy improvement of 6.41% to 15.83%.

Highlights

Land use and land cover (LULC) is a key driver of environmental change and can describe the conditions of changing phenomena resulting from human interaction with the environment [1]
maximum likelihood (ML), random forest (RF), and support vector machine (SVM) classifiers were used as an approach to classify LULC classes, 11 of which were used in the study (Table 1). e training sample and reference map were produced referring to the annual mosaic image data of SPOT 6/7 from 2019, with a Grid Feature Index (GIF) arrangement with a size of 2 km × 2 km
The MaSegFil approach (Figure 4) was used in a spatial filter stage in the postclassification of the digital classification results that are implemented in the LULC classification results from the ML, RF, and SVM classifiers

Summary

Introduction

Land use and land cover (LULC) is a key driver of environmental change and can describe the conditions of changing phenomena resulting from human interaction with the environment [1]. E object classification of LULC on the earth’s surface based on remote sensing data can be processed with two digital classification methods, namely, supervised and unsupervised classification [9]. E supervised version involves the classification of objects based on training sample input from object classes that appear on satellite images, which are run using an algorithm to generate LULC information. On the other hand, unsupervised classification involves data processing that can be conducted based on cluster pixel values in a satellite image (spectral, temporal, and spatial information) into value e Scientific World Journal classes, which are run using a clustering algorithm; these are iterative self-organizing data analysis [3, 25] and K-means clustering [26, 27]. LULC can be produced using a spatial filter to reduce this noise and to obtain better results

Results

Discussion

Conclusion