Ensemble Margin Based Semi-Supervised Random Forest for the Classification of Hyperspectral Image with Limited Training Data

2019 ◽  
Author(s):  
Wei Feng ◽  
Wenjiang Huang ◽  
Gabriel Dauphin ◽  
Junshi Xia ◽  
Yinghui Quan ◽  
...  
Keyword(s):  
Random Forest ◽  
Hyperspectral Image ◽  
Training Data

Classification of Fashion Article Images Based on Improved Random Forest and VGG-IE Algorithm

2019 ◽  
Vol 34 (03) ◽  
pp. 2051004
Author(s):  
Jian Liu ◽  
Yuchen Zheng ◽  
Ke Dong ◽  
Haitong Yu ◽  
Jianjun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Image Recognition ◽  
Classification Accuracy ◽  
Recommendation System ◽  
Computation Time ◽  
Training Data ◽  
Computational Time ◽  
Time Problem

In classification of fashion article images based on e-commerce image recommendation system, the classification accuracy and computation time cannot meet the actual requirements. Herein, for the first time to our knowledge, we present two diverse image recognition approaches for classification of fashion article images called random-forest method based on genetic algorithm (GA-RF) and Visual Geometry Group-Image Enhancement algorithm (VGG-IE) to solve classification accuracy and computation time problem. In GA-RF, the number of segmentation times and the decision trees are the key factors affecting the classification results. Improved genetic algorithm is introduced into the parameter optimization of forests to determine the optimal combination of the two parameters with minimal manual intervention. Finally, we propose six different Deep Neural Network architectures, including VGG-IE, to improve classification accuracy. The VGG-IE algorithm uses batch normalization and seven kinds training-data augmentation for ease and promotion of learning process. We investigate the effectiveness of the proposed method using Fashion-MNIST dataset and 70[Formula: see text]000 pictures, Experimental results demonstrate that, in comparison with the state-of-the-art algorithms for 10 categories of image recognition, our VGG algorithm has the shortest computational time when it satisfies certain classification accuracy. VGG-IE approach has the highest classification accuracy.

scholarly journals Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

2018 ◽  
Vol 10 (9) ◽  
pp. 1454 ◽  
Author(s):  
Mercedes Paoletti ◽  
Juan Haut ◽  
Javier Plaza ◽  
Antonio Plaza
Keyword(s):  
Neural Networks ◽  
Hyperspectral Image ◽  
Training Data ◽  
Current Layer ◽  
Feature Maps ◽  
Slowing Down ◽  
Spatial Features ◽  
Current State ◽  
Learning Rates

Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.

scholarly journals Morphological Attribute Profile Cube and Deep Random Forest for Small Sample Classification of Hyperspectral Image

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 117096-117108
Author(s):  
Bing Liu ◽  
Wenyue Guo ◽  
Xin Chen ◽  
Kuiliang Gao ◽  
Xibing Zuo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hyperspectral Image ◽  
Small Sample ◽  
Sample Classification ◽  
Morphological Attribute

scholarly journals Learning Deep Hierarchical Spatial–Spectral Features for Hyperspectral Image Classification Based on Residual 3D-2D CNN

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5276 ◽  
Author(s):  
Fan Feng ◽  
Shuangting Wang ◽  
Chunyang Wang ◽  
Jin Zhang
Keyword(s):  
Hyperspectral Image ◽  
Image Annotation ◽  
Small Sample ◽  
Training Data ◽  
Spectral Features ◽  
Training Samples ◽  
Small Sample Problem ◽  
Hyperspectral Classification ◽  
Organic Combination

Every pixel in a hyperspectral image contains detailed spectral information in hundreds of narrow bands captured by hyperspectral sensors. Pixel-wise classification of a hyperspectral image is the cornerstone of various hyperspectral applications. Nowadays, deep learning models represented by the convolutional neural network (CNN) provides an ideal solution for feature extraction, and has made remarkable achievements in supervised hyperspectral classification. However, hyperspectral image annotation is time-consuming and laborious, and available training data is usually limited. Due to the “small-sample problem”, CNN-based hyperspectral classification is still challenging. Focused on the limited sample-based hyperspectral classification, we designed an 11-layer CNN model called R-HybridSN (Residual-HybridSN) from the perspective of network optimization. With an organic combination of 3D-2D-CNN, residual learning, and depth-separable convolutions, R-HybridSN can better learn deep hierarchical spatial–spectral features with very few training data. The performance of R-HybridSN is evaluated over three public available hyperspectral datasets on different amounts of training samples. Using only 5%, 1%, and 1% labeled data for training in Indian Pines, Salinas, and University of Pavia, respectively, the classification accuracy of R-HybridSN is 96.46%, 98.25%, 96.59%, respectively, which is far better than the contrast models.

scholarly journals South German Credit Data Classification Using Random Forest Algorithm to Predict Bank Credit Receipts

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Yoga Religia ◽  
Gatot Tri Pranoto ◽  
Egar Dika Santosa
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Large Data ◽  
Classification Algorithm ◽  
Large Data Sets ◽  
Training Data ◽  
Data Sets ◽  
Testing Data ◽  
Credit Data

Normally, most of the bank's wealth is obtained from providing credit loans so that a marketing bank must be able to reduce the risk of non-performing credit loans. The risk of providing loans can be minimized by studying patterns from existing lending data. One technique that can be used to solve this problem is to use data mining techniques. Data mining makes it possible to find hidden information from large data sets by way of classification. The Random Forest (RF) algorithm is a classification algorithm that can be used to deal with data imbalancing problems. The purpose of this study is to discuss the use of the RF algorithm for classification of South German Credit data. This research is needed because currently there is no previous research that applies the RF algorithm to classify South German Credit data specifically. Based on the tests that have been done, the optimal performance of the classification algorithm RF on South German Credit data is the comparison of training data of 85% and testing data of 15% with an accuracy of 78.33%.

scholarly journals CLASSIFICATION OF AERIAL LASER SCANNING POINT CLOUDS USING MACHINE LEARNING: A COMPARISON BETWEEN RANDOM FOREST AND TENSORFLOW

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1105-1111 ◽  
Author(s):  
F. Pirotti ◽  
F. Tonion
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Training Data ◽  
Semantic Classification ◽  
The Neural Network

<p><strong>Abstract.</strong> In this investigation a comparison between two machine learning (ML) models for semantic classification of an aerial laser scanner point cloud is presented. One model is Random Forest (RF), the other is a multi-layer neural network, TensorFlow (TF). Accuracy results were compared over a growing set of training data, using a stratified independent sampling over classes from 5% to 50% of the total dataset. Results show RF to have average F1&amp;thinsp;=&amp;thinsp;0.823 for the 9 classes considered, whereas TF had average F1&amp;thinsp;=&amp;thinsp;0.450. F1 values where higher for RF than TF, due to complexity in the determination of a suitable composition of the hidden layers of the neural network in TF, and this can likely be improved to reach higher accuracy values. Further study in this sense is planned.</p>

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

Sign in / Sign up

Export Citation Format

Share Document