Lithostratigraphic Classification Method Combining Optimal Texture Window Size Selection and Test Sample Purification Using Landsat 8 OLI Data

Abstract Gray Level Co-Occurrence Matrix (GLCM), as a measure of spatial features has been used as supplemental information to improve image classification accuracy for lithological recognition. Window size is an important parameter for texture extraction, which will affect the extracted texture results. Besides, the existence of mixed pixels in image usually causes errors in test samples, which significantly influences the credibility of accuracy assessment. Thus, this paper proposes a lithological classification method combined with optimal texture window size selection and test sample purification. Firstly, optimal window size pre-estimated based on semivariogram was used to calculated GLCM texture of image. Secondly, based on multidimensional textural and spectral features, a support vector machine (SVM) classifier was employed to classify the image. Thirdly, using the proposed sample purification method and textural features of image, sample purification rules were created based on attribute coherence to remove the test sample points that conflicted with the rules. Finally, the validity of the semivariogram-based texture extraction window selection was verified by classifications based on Angular Second Moment (ASM) of different window sizes combined with spectral features. Also, the accuracies between different combinations of classifications were assessed by test samples with and without sample purification. Experimental results show that the pre-estimated texture window size can guarantee a classification result with high classification accuracy for lithological classification. The results also demonstrated that the accuracy of lithological classification based on spectral features and ASM textural features was the highest. The overall lithological classification accuracy and kappa value, without sample purification selected by stratified sampling, were respectively 87.4% and 0.84, however those with sample purification were respectively 88.01% and 0.85. The results show that the proposed method is capable of yielding more reliable lithostratigraphic identification.

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Research on Classification of Remote Sensing Image Based on SVM Including Textural Features

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.2559 ◽

2014 ◽

Vol 543-547 ◽

pp. 2559-2565 ◽

Cited By ~ 2

Author(s):

Feng Hua Huang

Keyword(s):

Remote Sensing ◽

Land Use ◽

Support Vector Machine ◽

Remote Sensing Image ◽

Classification Method ◽

Likelihood Method ◽

Support Vector ◽

Textural Features ◽

Spectral Features ◽

Experimental Area

In order to solve the problems in the traditional remote sensing image based on spectral information, such as low classification accuracy, different object with the same spectral features or the same object with the different spectral features, and limited sample quantity and so on, a remote sensing image classification method based on the support vector machine (SVM) including with textural features is proposed. Using Langqi Island of Fuzhou as experimental area, preprocessing and principal component analysis were made to initialize TM images, and the spectral features and GLCM-based textural features of ground objects were extracted and analyzed respectively. Then, the extraction, training and testing of samples based on the two types of features were finished for training various SVM classifiers, which were used for classifying land use in the experimental area. Through the maximum likelihood method, the BP neural network and the support vector machine (SVM), a crossed classification and contrast experiment was made to two different types of samples based on the simple spectral features and the features combined with texture respectively. The experimental results showed that the SVM classification method including textural features can effectively improve the accuracy of land use classification, and therefore it can be promoted better.

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An emotion classification method from electroencephalogram based on 1/f fluctuation theory

Measurement and Control ◽

10.1177/0020294020913893 ◽

2020 ◽

Vol 53 (5-6) ◽

pp. 824-832

Author(s):

Hao Li ◽

Xia Mao ◽

Lijiang Chen

Keyword(s):

Real Time ◽

Classification Accuracy ◽

Support Vector Machine Classifier ◽

Fluctuation Theory ◽

Classification Method ◽

Support Vector ◽

Signal Acquisition ◽

Emotion Classification ◽

The Real ◽

Time Performance

Electroencephalogram data are easily affected by artifacts, and a drift may occur during the signal acquisition process. At present, most research focuses on the automatic detection and elimination of artifacts in electrooculograms, electromyograms and electrocardiograms. However, electroencephalogram drift data, which affect the real-time performance, are mainly manually calibrated and abandoned. An emotion classification method based on 1/f fluctuation theory is proposed to classify electroencephalogram data without removing artifacts and drift data. The results show that the proposed method can still achieve a great classification accuracy of 75% in cases in which artifacts and drift data exist when using the support vector machine classifier. In addition, the real-time performance of the proposed method is guaranteed.

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Research on Automatic Classification Method of Ethnic Music Emotion Based on Machine Learning

Journal of Mathematics ◽

10.1155/2022/7554404 ◽

2022 ◽

Vol 2022 ◽

pp. 1-11

Author(s):

Zijin Wu

Keyword(s):

Folk Music ◽

Classification Accuracy ◽

Fine Tuning ◽

Classification Model ◽

Classification Method ◽

Support Vector ◽

Ethnic Music ◽

Huge Impact ◽

Speed Up

With the development of the country’s economy, there is a flourishing situation in the field of culture and art. However, the diversification of artistic expressions has not brought development to folk music. On the contrary, it brought a huge impact, and some national music even fell into the dilemma of being lost. This article is mainly aimed at the recognition and classification of folk music emotions and finds the model that can make the classification accuracy rate as high as possible. The classification model used in this article is mainly after determining the use of Support Vector Machine (SVM) classification method, a variety of attempts have been made to feature extraction, and good results have been achieved. Explore the Deep Belief Network (DBN) pretraining and reverse fine-tuning process, using DBN to learn the fusion characteristics of music. According to the abstract characteristics learned by them, the recognition and classification of folk music emotions are carried out. The DBN is improved by adding “Dropout” to each Restricted Boltzmann Machine (RBM) and adjusting the increase standard of weight and bias. The improved network can avoid the overfitting problem and speed up the training of the network. Through experiments, it is found that using the fusion features proposed in this paper, through classification, the classification accuracy has been improved.

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EFFECT OF PANSHARPENED IMAGE ON SOME OF PIXEL BASED AND OBJECT BASED CLASSIFICATION ACCURACY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b7-235-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 235-239 ◽

Cited By ~ 1

Author(s):

P. Karakus ◽

H. Karabork

Keyword(s):

Spatial Resolution ◽

Spectral Resolution ◽

Classification Accuracy ◽

Multispectral Image ◽

Classification Method ◽

Support Vector ◽

Classification Methods ◽

Panchromatic Image ◽

Object Based ◽

Spot 5

Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

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Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification

Mathematical Problems in Engineering ◽

10.1155/2020/8065396 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Yanling Han ◽

Cong Wei ◽

Ruyan Zhou ◽

Zhonghua Hong ◽

Yun Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Image Classification ◽

Classification Accuracy ◽

Spatial Information ◽

Spatial Spectrum ◽

Small Samples ◽

Support Vector ◽

Spectral Features ◽

3D Cnn

Sea ice is one of the most prominent marine disasters in high latitudes. Remote sensing technology provides an effective means for sea ice detection. Remote sensing sea ice images contain rich spectral and spatial information. However, most traditional methods only focus on spectral information or spatial information, and do not excavate the feature of spectral and spatial simultaneously in remote sensing sea ice images classification. At the same time, the complex correlation characteristics among spectra and small sample problem in sea ice classification also limit the improvement of sea ice classification accuracy. For this issue, this paper proposes a new remote sensing sea ice image classification method based on squeeze-and-excitation (SE) network, convolutional neural network (CNN), and support vector machines (SVMs). The proposed method designs 3D-CNN deep network so as to fully exploit the spatial-spectrum features of remote sensing sea ice images and integrates SE-Block into 3D-CNN in-depth network in order to distinguish the contributions of different spectra to sea ice classification. According to the different contributions of spectral features, the weight of each spectral feature is optimized by fusing SE-Block in order to further enhance the sample quality. Finally, information-rich and representative samples are chosen by combining the idea of active learning and input into SVM classifier, and this achieves superior classification accuracy of remote sensing sea ice images with small samples. In order to verify the effectiveness of the proposed method, we conducted experiments on three different data from Baffin Bay, Bohai Bay, and Liaodong Bay. The experimental results show that compared with other classical classification methods, the proposed method comprehensively considers the correlation among spectral features and the small samples problems and deeply excavates the spatial-spectrum characteristics of sea ice and achieves better classification performance, which can be effectively applied to remote sensing sea ice image classification.

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Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area

10.20944/preprints201709.0009.v1 ◽

2017 ◽

Author(s):

Francisco Gomariz-Castillo ◽

Francisco Alonso-Sarría ◽

Fulgencio Cánovas-García

Keyword(s):

Maximum Likelihood ◽

Random Forest ◽

Classification Accuracy ◽

Maximum A Posteriori ◽

Support Vector ◽

Textural Features ◽

A Posteriori ◽

Semiarid Area ◽

Vector Machines ◽

Terrain Features

The aim of this study is to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area. i) Using different classification algorithms: Maximum Likelihood, Random Forest, Support Vector Machines and Sequential Maximum a Posteriori, with parameter optimisation in the second and third cases; ii) using different feature sets: spectral features, spectral and textural features, and spectral, textural and terrain features; and iii) using different image-sets: winter, spring, summer, autumn, winter+summer, winter+ spring+summer; and a four seasons combination. A 3-way ANOVA is used to discern which of these approaches and their interactions significantly increases accuracy. Tukey-Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM, and OLI images corresponding to the period 2000-2015. A combination of four images was the best way to improve accuracy. Maximum Likelihood, Random Forest and Support Vector Machines do not significantly increase accuracy when textural information is added, but do so when terrain features are taken into account. On the other hand, Sequential Maximum a Posteriori increases accuracy when textural features are used, but reduces accuracy substantially when terrain features are included. Random Forest using the three feature subsets and Sequential Maximum a Posteriori with spectral and textural features had the largest kappa values, around 0.9.

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One-Class Fault Detection Using Multi-Layer Elm-Based Auto-Encoder

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026821500012 ◽

2020 ◽

pp. 2150001

Author(s):

Li Wuke ◽

Yin Guangluan ◽

Chen Xiaoxiao

Keyword(s):

Fault Detection ◽

Classification Accuracy ◽

Test Sample ◽

Reconstruction Error ◽

Support Vector ◽

Testing Time ◽

Decision Threshold ◽

Learning Machine ◽

One Class Classification ◽

And Function

A new approach for one-class fault detection trained only by normal samples has been proposed in this paper. The approach contains multi-anterior-layers for feature extraction and one post-layer for one-class classification. The multi-anterior-layers are based on extreme learning machine-based auto-encoder (ELM-AE). Multi-ELM-AEs are stacked in the front hidden layers to extract abstract features from the raw input. The post-layer is based on the reconstruction error-based ELM-AE (Re-ELM-AE) to act as one-class classifier. As the extension of ELM-AE, the decision threshold and function are given in the Re-ELM-AE, which are utilized to identify whether the test sample is faulty. The efficacy of the presented algorithm is demonstrated on a mathematic example and fault dataset from motor bearing. The method has been compared with shallow learning methods such as one-class support vector machine (OCSVM), the Re-ELM-AE, and one multi-layer neural network named stacked auto-encoder (SAE). The experiment results show that the proposed method outperforms OCSVM and Re-ELM-AE in classification accuracy. Though the classification accuracy of the proposed method and SAE is similar, the training and testing time of the proposed method is much lower than SAE.

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URBAN MATERIAL CLASSIFICATION USING SPECTRAL AND TEXTURAL FEATURES RETRIEVED FROM AUTOENCODERS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-1-2020-25-2020 ◽

2020 ◽

Vol V-1-2020 ◽

pp. 25-32

Author(s):

R. Ilehag ◽

J. Leitloff ◽

M. Weinmann ◽

A. Schenk

Keyword(s):

Classification Tree ◽

Remote Sensing Data ◽

Hyperspectral Data ◽

Gradient Boosting ◽

Support Vector ◽

Textural Features ◽

Spectral Features ◽

Material Classification ◽

Characteristic Features ◽

Spectral Libraries

Abstract. Classification of urban materials using remote sensing data, in particular hyperspectral data, is common practice. Spectral libraries can be utilized to train a classifier since they provide spectral features about selected urban materials. However, urban materials can have similar spectral characteristic features due to high inter-class correlation which can lead to misclassification. Spectral libraries rarely provide imagery of their samples, which disables the possibility of classifying urban materials with additional textural information. Thus, this paper conducts material classification comparing the benefits of using close-range acquired spectral and textural features. The spectral features consist of either the original spectra, a PCA-based encoding or the compressed spectral representation of the original spectra retrieved using a deep autoencoder. The textural features are generated using a deep denoising convolutional autoencoder. The spectral and textural features are gathered from the recently published spectral library KLUM. Three classifiers are used, the two well-established Random Forest and Support Vector Machine classifiers in addition to a Histogram-based Gradient Boosting Classification Tree. The achieved overall accuracy was within the range of 70–80% with a standard deviation between 2–10% across all classification approaches. This indicates that the amount of samples still is insufficient for some of the material classes for this classification task. Nonetheless, the classification results indicate that the spectral features are more important for assigning material labels than the textural features.

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Optimal filter bank representation in texture classification using support vector machines

10.32920/ryerson.14645844.v1 ◽

2021 ◽

Author(s):

Samsher Singh Sidhu

Keyword(s):

Support Vector Machines ◽

Texture Analysis ◽

Classification Accuracy ◽

Texture Classification ◽

Classification Method ◽

Support Vector ◽

Discrete Wavelet ◽

Translation Invariant ◽

Vector Machines ◽

Wavelet Frame Transform

Texture analysis has been a field of study for over three decades in many fields including electrical engineering. Today, texture analysis plays a crucial role in many tasks ranging from remote sensing to medical imaging. Researchers in this field have dealt with many different approaches, all trying to achieve the goal of high classification accuracy. The main difficulty of texture analysis was the lack of ability of the tools to characterize adequately different scales of the textures effectively. The development in multi-resolution analysis such as Gabor and Wavelet Transform help to overcome this difficulty. This thesis describes the texture classification algorithm that uses the combination of statistical features and co-occurrence features of the Discrete Wavelet Transformed images. The classification accuracy is increased by using translation-invariant features generated from the Discrete Wavelet Frame Transform. The results are further improved by focussing on the transformed images used for feature extraction by using filters which essentially extract those areas of the image that discriminate themselves from other image classes. In effect, by reducing the spatial characteristics of images that contribute to the features, the texture classification method still has the ability to preserve the classification accuracy. Support Vector Machines has proved excellent performance in the area of pattern recognition problems. We have applied SVMs with the texture classification method described above and, when compared to traditional classifiers, SVM has produced more accurate classification results on the Brodatz texture album.

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