Classification of textures in satellite image with Gabor filters and a multi layer perceptron with back propagation algorithm obtaining high accuracy

Beluco, Adriano; Engel, Paulo Martins; Beluco, Alexandre

doi:10.5935/2076-2909.20150001

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…One commonly used type of neural network is a multilayered feed-forward perceptron that consists of several layers of neurons connected with each other. The multilayered perceptron can separate data that are nonlinear and generally consists of three or more types of layers (Beluco, Engel, & Beluco, 2015). ANNs have successfully been applied to remote sensing in many fields (Mas & Flores, 2008;Zagajewski, 2010;Zhang & Xie, 2012).…”

Section: Methodsmentioning

confidence: 99%

Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images

Raczko

Zagajewski

2017

European Journal of Remote Sensing

287

176

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Knowledge of tree species composition in a forest is an important topic in forest management. Accurate tree species maps allow for much more detailed and in-depth analysis of biophysical forest variables. The paper presents a comparison of three classification algorithms: support vector machines (SVM), random forest (RF) and artificial neural networks (ANN) for tree species classification using airborne hyperspectral data from the Airborne Prism EXperiment sensor. The aim of this paper is to evaluate the three nonparametric classification algorithms (SVM, RF and ANN) in an attempt to classify the five most common tree species of the Szklarska Poręba area: spruce (Picea alba L. Karst), larch (Larix decidua Mill.), alder (Alnus Mill), beech (Fagus sylvatica L.) and birch (Betula pendula Roth). To avoid human introduced biases a 0.632 bootstrap procedure was used during evaluation of each compared classifier. Of all compared classification results, ANN achieved the highest median overall classification accuracy (77%) followed by SVM with 68% and RF with 62%. Analysis of the stability of results concluded that RF and SVM had the lowest variance of overall accuracy and kappa coefficient (12 percentage points) while ANN had 15 percentage points variance in results.

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Section: Methodsmentioning

confidence: 99%

Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images

Raczko

Zagajewski

2017

European Journal of Remote Sensing

287

176

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“…One commonly used type of neural network is a multilayered feed-forward perceptron that consists of several layers of neurons connected with each other (Figure 4). The multilayered perceptron can separate data that are nonlinear and generally consists of three or more types of layers [58].…”

Section: Neural Networkmentioning

confidence: 99%

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

Boateng¹,

Otoo²,

Abaye³

2020

JDAIP

145

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In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows,

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“…The RF classifier is commonly described as an ensemble of decision trees where class labeling is achieved by voting. It can handle high-dimensional data and is relatively resistant to overfitting [54]. RF also determines the importance of features (texture, spectral, and indices features) in the classification process [55,56].…”

Section: Study Area and Materialsmentioning

confidence: 99%

Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Sun

Bian

Zhou

et al. 2019

Sensors

118

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Crop-type identification is very important in agricultural regions. Most researchers in this area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to identify crops. This paper uses multi-source (Sentinel-1, Sentinel-2, and Landsat-8) and multi-temporal data to identify crop types. The change detection method was used to analyze spectral and indices information in time series. Significant differences in crop growth status during the growing season were found. Then, three obviously differentiated time features were extracted. Three advanced machine learning algorithms (Support Vector Machine, Artificial Neural Network, and Random Forest, RF) were used to identify the crop types. The results showed that the detection of (Vertical-vertical) VV, (Vertical-horizontal) VH, and Cross Ratio (CR) changes was effective for identifying land cover. Moreover, the red-edge changes were obviously different according to crop growth periods. Sentinel-2 and Landsat-8 showed different normalized difference vegetation index (NDVI) changes also. By using single remote sensing data to classify crops, Sentinel-2 produced the highest overall accuracy (0.91) and Kappa coefficient (0.89). The combination of Sentinel-1, Sentinel-2, and Landsat-8 data provided the best overall accuracy (0.93) and Kappa coefficient (0.91). The RF method had the best performance in terms of identity classification. In addition, the indices feature dominated the classification results. The combination of phenological period information with multi-source remote sensing data can be used to explore a crop area and its status in the growing season. The results of crop classification can be used to analyze the density and distribution of crops. This study can also allow to determine crop growth status, improve crop yield estimation accuracy, and provide a basis for crop management.

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Classification of textures in satellite image with Gabor filters and a multi layer perceptron with back propagation algorithm obtaining high accuracy

Cited by 6 publications

References 21 publications

Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images

Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

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