Performance Analysis of Advanced Decision Forest Algorithms in Hyperspectral Image Classification

2020 ◽  
Vol 86 (9) ◽  
pp. 571-580
Author(s):  
Ismail Colkesen ◽  
Omer Habib Ertekin
Keyword(s):  
Infrared Imaging ◽  
Hyperspectral Image ◽  
Confusion Matrix ◽  
Data Sets ◽  
Rotation Forest ◽  
Imaging Spectrometer ◽  
Error Diagram ◽  
Diversity Measures ◽  
Kennedy Space

In this study, the performances of random forest (<small>RF</small>), rotation forest (<small>RoF</small>), and canonical correlation forest (<small>CCF</small>) algorithms were compared and analyzed for classification of hyperspectral imagery. For this purpose, the Airborne Visible/Infrared Imaging Spectrometer (<small>AVIRIS</small>) Indian Pine (<small>IP</small>), the Reflective Optics System Imaging Spectrometer University of Pavia, and the AVIRIS Kennedy Space Center (<small>KSC</small>) data sets were used as main data sources. In addition to the confusion matrix–derived accuracy measures (overall accuracy, kappa coefficient, F-scores), the performances of the algorithms were analyzed in detail considering three diversity measures (Q statistics, correlations, and interrater agreements) and a kappa-error diagram. Results showed that the highest classification accuracies (87% for IP, 94% for PU, and 93% for KSC data sets) were achieved with the use of CCF algorithm, and improvements in classification accuracy were statistically significant compared to RF and RoF. Based on the diversity measures and the kappa-error diagram, individual learners in the CCF ensemble were found to be more diverse and accurate.

scholarly journals Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5559
Author(s):  
Na Li ◽  
Ruihao Wang ◽  
Huijie Zhao ◽  
Mingcong Wang ◽  
Kewang Deng ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Infrared Imaging ◽  
Hyperspectral Image ◽  
Matching Pursuit ◽  
Small Sample Size ◽  
Small Sample ◽  
Hyperspectral Data ◽  
Classification Method ◽  
Diverse Density

To solve the small sample size (SSS) problem in the classification of hyperspectral image, a novel classification method based on diverse density and sparse representation (NCM_DDSR) is proposed. In the proposed method, the dictionary atoms, which learned from the diverse density model, are used to solve the noise interference problems of spectral features, and an improved matching pursuit model is presented to obtain the sparse coefficients. Airborne hyperspectral data collected by the push-broom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS) are applied to evaluate the performance of the proposed classification method. Results illuminate that the overall accuracies of the proposed model for classification of PHI and AVIRIS images are up to 91.59% and 92.83% respectively. In addition, the kappa coefficients are up to 0.897 and 0.91.

scholarly journals Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3601 ◽  
Author(s):  
Fei Lv ◽  
Min Han
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Image Data ◽  
Classification Method ◽  
Data Sets ◽  
Hyperspectral Image Classification ◽  
Rotation Forest ◽  
Public Data ◽  
Learning Machine ◽  
Q Statistic

Hyperspectral image classification is a hot issue in the field of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classification method that is used to process image data. In this paper, an efficient hyperspectral image classification method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non-negative matrix factorization( NMF) is used to do feature segmentation in order to get more effective data. Secondly, kernel extreme learning machine (KELM) is chosen as base classifier to improve the classification efficiency. The proposed method inherits the advantages of KELM and has an analytic solution to directly implement the multiclass classification. Then, Q-statistic is used to select base classifiers. Finally, the results are obtained by using the voting method. Three simulation examples, classification of AVIRIS image, ROSIS image and the UCI public data sets respectively, are conducted to demonstrate the effectiveness of the proposed method.

scholarly journals Alternately Updated Spectral–Spatial Convolution Network for the Classification of Hyperspectral Images

2019 ◽  
Vol 11 (15) ◽  
pp. 1794 ◽  
Author(s):  
Wenju Wang ◽  
Shuguang Dou ◽  
Sen Wang
Keyword(s):  
Deep Learning ◽  
Forward Direction ◽  
Hyperspectral Images ◽  
Data Sets ◽  
Convolutional Network ◽  
Spatial Features ◽  
Training Samples ◽  
Feedback Structure ◽  
Kennedy Space

The connection structure in the convolutional layers of most deep learning-based algorithms used for the classification of hyperspectral images (HSIs) has typically been in the forward direction. In this study, an end-to-end alternately updated spectral–spatial convolutional network (AUSSC) with a recurrent feedback structure is used to learn refined spectral and spatial features for HSI classification. The proposed AUSSC includes alternating updated blocks in which each layer serves as both an input and an output for the other layers. The AUSSC can refine spectral and spatial features many times under fixed parameters. A center loss function is introduced as an auxiliary objective function to improve the discrimination of features acquired by the model. Additionally, the AUSSC utilizes smaller convolutional kernels than other convolutional neural network (CNN)-based methods to reduce the number of parameters and alleviate overfitting. The proposed method was implemented on four HSI data sets, as follows: Indian Pines, Kennedy Space Center, Salinas Scene, and Houston. Experimental results demonstrated that the proposed AUSSC outperformed the HSI classification accuracy obtained by state-of-the-art deep learning-based methods with a small number of training samples.

Imbalanced Multi-Class Classification of Hyperspectral Image Based on Smote and Deep Rotation Forest

2021 ◽  
Author(s):  
Xian Zhong ◽  
Yinghui Quan ◽  
Wei Feng ◽  
Qiang Li ◽  
Gabriel Dauphin ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Rotation Forest ◽  
Multi Class Classification

scholarly journals Unsupervised Clustering for Hyperspectral Images

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 277 ◽  
Author(s):  
Laura Bilius ◽  
Stefan Pentiuc
Keyword(s):  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Data Sets ◽  
Hyperspectral Image Classification ◽  
Spectral Bands ◽  
Long Time ◽  
Parafac Decomposition ◽  
Classification Time

Hyperspectral images are becoming a valuable tool much used in agriculture, mineralogy, and so on. The challenge is to successfully classify the materials founded in the field relevant for different applications. Due to a large amount of data corresponding to a big number of spectral bands, the classification programs require a long time to analyze and classify the data. The purpose is to find a better method for reducing the classification time. We exploit various algorithms on real hyperspectral data sets to find out which algorithm is more effective. This paper presents a comparison of unsupervised hyperspectral image classification such as K-means, Hierarchical clustering, and Parafac decomposition, which allows the performance of the model reduction and feature extraction. The results showed that the method useful for big data is the classification of data after Parafac Decomposition.

Hyperspectral Image Classification Using Spatial and Edge Features Based on Deep Learning

2019 ◽  
Vol 33 (09) ◽  
pp. 1954027 ◽  
Author(s):  
Dexiang Zhang ◽  
Jingzhong Kang ◽  
Lina Xun ◽  
Yu Huang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Bilateral Filter ◽  
Convolution Neural Network ◽  
Hyperspectral Images ◽  
Data Sets ◽  
Edge Information

In recent years, deep learning has been widely used in the classification of hyperspectral images and good results have been achieved. But it is easy to ignore the edge information of the image when using the spatial features of hyperspectral images to carry out the classification experiments. In order to make full use of the advantages of convolution neural network (CNN), we extract the spatial information with the method of minimum noise fraction (MNF) and the edge information by bilateral filter. The combination of the two kinds of information not only increases the useful information but also effectively removes part of the noise. The convolution neural network is used to extract features and classify for hyperspectral images on the basis of this fused information. In addition, this paper also uses another kind of edge-filtering method to amend the final classification results for a better accuracy. The proposed method was tested on three public available data sets: the University of Pavia, the Salinas, and the Indian Pines. The competitive results indicate that our approach can realize a classification of different ground targets with a very high accuracy.

scholarly journals Classification Endmember Selection with Multi-Temporal Hyperspectral Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1575
Author(s):  
Tingxuan Jiang ◽  
Harald van der Werff ◽  
Freek van der Meer
Keyword(s):  
Classification Accuracy ◽  
Infrared Imaging ◽  
Reference Data ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Single Image ◽  
Hyperspectral Image Classification ◽  
Imaging Spectrometer ◽  
Spectral Angle Mapper ◽  
Multi Temporal

In hyperspectral image classification, so-called spectral endmembers are used as reference data. These endmembers are either extracted from an image or taken from another source. Research has shown that endmembers extracted from an image usually perform best when classifying a single image. However, it is unclear if this also holds when classifying multi-temporal hyperspectral datasets. In this paper, we use spectral angle mapper, which is a frequently used classifier for hyperspectral datasets to classify multi-temporal airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral imagery. Three classifications are done on each of the images with endmembers being extracted from the corresponding image, and three more classifications are done on the three images while using averaged endmembers. We apply image-to-image registration and change detection to analyze the consistency of the classification results. We show that the consistency of classification accuracy using the averaged endmembers (around 65%) outperforms the classification results generated using endmembers that are extracted from each image separately (around 40%). We conclude that, for multi-temporal datasets, it is better to have an endmember collection that is not directly from the image, but is processed to a representative average.

Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification

2020 ◽  
Vol 4 (2) ◽  
pp. 377-383
Author(s):  
Eko Laksono ◽  
Achmad Basuki ◽  
Fitra Bachtiar
Keyword(s):  
Frequency Distribution ◽  
Confusion Matrix ◽  
High Accuracy ◽  
K Value ◽  
A Value ◽  
Optimal Value ◽  
Classification Evaluation ◽  
Email Spam ◽  
Email Classification

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

Sign in / Sign up

Export Citation Format

Share Document