Performance evaluation of MLE, RF and SVM classification algorithms for watershed scale land use/land cover mapping using sentinel 2 bands

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

“…Spatially, the classification results are shown in Figures 5(a) – 5(c) , 6(a) – 6(c) , and 7(a) – 7(c) . The accuracy of the assessment results for RF and SVM is similar; it has been reported by Rana and Venkata Suryanarayana [ 45 ] and Phan et al [ 36 ] that RF and SVM are the latest developments in the computational aspect of image classification and can minimise errors in classification, making them superior to parametric classifiers such as ML.…”

“…If there are two nonlinear classes, the SVM classifier approach tries to find a hyperplane that maximises margins and minimises a quantity proportional to the number of misclassification errors. In detail, the SVM classifier formulation for linearly inseparable data to find the separating hyperplane is presented as follows [ 21 , 44 , 45 ]: where x is the input data in the input space I into a high dimension space H and ϕ ( x ) is the kernel function.…”

“…Accuracy assessment was made to evaluate the results of the digital classifications generated from the ML, RF, and SVM classifiers, together with the optimisation results from using the MaSegFil approach for the postclassification stage as a spatial filter, as proposed in this study. A confusion matrix was used to evaluate the accuracy assessment procedure, which took into account user accuracy, producer accuracy, kappa, and overall accuracy [ 36 , 42 , 45 – 47 ].…”

A new feature extraction approach based on non linear source separation

“…Existant works are based on spectral and spatial metrics [1]- [4], handcrafted feature extraction [1], [5] and semantic deep feature extraction [6]- [8]. Combining various feature extraction models has also been investigated to emprove the data representation efficieny [1], [4], [8]. In contrast to convenctional methods that emply spectral or spatial data representation for land cover identification, a significant advantage of Int J Elec & Comp Eng ISSN: 2088-8708 …”

“…Table 1. Features extraction approaches for land cover classification Spatial metrics and Texture measures for land cover objects classification [1] Parcels geometrical attributes including shape, height, proximity to major roads, similarity to neighbors [2] Spectral indices (NDVI, MNDWI, NDBI) [3] Object-based feature extraction based on spatial and spectral statistics [4] High dimensional feature vector combining focal textures statistics (median, mean, and standard deviation) and Gray Level Co-occurrence Matrix derived in different kernel sizes [5] Semantic features extraction using different deep convolutional neural networks models [6] High-level semantic features extraction based on transfer learning and the Inception-ResNet-v2 model [7] Deep semantic feature extraction using different models (VGG-S , VGG-M, VGG-F, VGG-VD16, VGG-VD16) [8] Combining deep semantic features, spectral features and GLCM texture features [9] Vision-based technology have been widely used for object of interest detection. For instance, in [10] the detecting task is based on histogram equalization and morphological processings.…”