scholarly journals Hyperspectral Image Denoising and Classification Using Multi-Scale Weighted EMAPs and Extreme Learning Machine

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2137
Author(s):  
Meizhuang Liu ◽  
Faxian Cao ◽  
Zhijing Yang ◽  
Xiaobin Hong ◽  
Yuezhen Huang
Keyword(s):  
Extreme Learning Machine ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Decision Fusion ◽  
Joint Decision ◽  
Multiple Attribute ◽  
Mean Filter ◽  
Learning Machine ◽  
Elm Classifier ◽  
Homogeneous Regions

Recently, extended multi-attribute profiles (EMAPs) have attracted much attention due to its good performance while applied to remote sensing images feature extraction and classification. Since the EMAPs connect multiple attribute features without considering the pixel-based Hyperspectral Image (HSI) classification, homogeneous regions may become unsmooth due to the noise to be introduced. To tackle this problem, we propose the weighted EMAPs (WEMAPs) to reduce the noise and smoothen the homogeneous regions based on weighted mean filter (WMF). Then, we construct multiscale WEMAPs to product multiscale feature in order to extract different spatial structures of the HSI and produce better classification results. Finally, a new joint decision fusion and feature fusion (JDFFF) framework is proposed based on the decision fusion (DF) and the multiscale WEMAPs (MWEMAPs) based on extreme learning machine (ELM) classifier. That is, the classification results from various scales are combined into a final one with ELM to perform the HSI classification. Experiment results show that the proposed algorithm significantly outperforms many state-of-the-art HSI classification algorithms.

scholarly journals A microarray gene expressions with classification using extreme learning machine

Genetika ◽  
2015 ◽  
Vol 47 (2) ◽  
pp. 523-534
Author(s):  
M. Yasodha ◽  
P. Ponmuthuramalingam
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Dna Microarrays ◽  
Research Work ◽  
The Body ◽  
Cancer Classification ◽  
Medical Sciences ◽  
Gene Expressions ◽  
Learning Machine ◽  
Elm Classifier

In the present scenario, one of the dangerous disease is cancer. It spreads through blood or lymph to other location of the body, it is a set of cells display uncontrolled growth, attack and destroy nearby tissues, and occasionally metastasis. In cancer diagnosis and molecular biology, a utilized effective tool is DNA microarrays. The dominance of this technique is recognized, so several open doubt arise regarding proper examination of microarray data. In the field of medical sciences, multicategory cancer classification plays very important role. The need for cancer classification has become essential because the number of cancer sufferers is increasing. In this research work, to overcome problems of multicategory cancer classification an improved Extreme Learning Machine (ELM) classifier is used. It rectify problems faced by iterative learning methods such as local minima, improper learning rate and over fitting and the training completes with high speed.

scholarly journals FPGA Implementation of Weighted Online Sequential Extreme Learning Machine for Data Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 6551-6557
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Bench Mark ◽  
Hardware Platform ◽  
Learning Speed ◽  
Field Programmable ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
Elm Classifier ◽  
Short Event

To design an efficient embedded module field-programmable gate array (FPGA) plays significant role. FPGA, a high speed reconfigurable hardware platform has been used in various field of research to produce the throughput efficiently. A now-a-days artificial neural network (ANN) is the most prevalent classifier for many analytical applications. In this paper, weighted online sequential extreme learning machine (WOS-ELM) classifier is presented and implemented in hardware environment to classify the different real-world bench-mark datasets. The faster learning speed, remarkable classification accuracy, lesser hardware resources, and short-event detection time, aid the hardware implementation of WOS-ELM classifier to design an embedded module. Finally, the developed hardware architecture of the WOS-ELM classifier is implemented on a high speed reconfigurable Xilinx Virtex (ML506) FPGA board to demonstrate the feasibility, effectiveness, and robustness of WOS-ELM classifier to classify the data in real-time environment.

scholarly journals Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1262 ◽  
Author(s):  
Xiaoping Fang ◽  
Yaoming Cai ◽  
Zhihua Cai ◽  
Xinwei Jiang ◽  
Zhikun Chen
Keyword(s):  
Multiobjective Optimization ◽  
Extreme Learning Machine ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Spectral Band ◽  
Feature Learning ◽  
Pareto Solution ◽  
Conflicting Objectives ◽  
Learning Machine ◽  
Hidden Layer

Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.

Hyperspectral image classification using FPCA-based kernel extreme learning machine

Optik ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3942-3948 ◽  
Author(s):  
Yantao Wei ◽  
Guangrun Xiao ◽  
He Deng ◽  
Hong Chen ◽  
Mingwen Tong ◽  
...  
Keyword(s):  
Image Classification ◽  
Extreme Learning Machine ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

scholarly journals A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yunlong Yu ◽  
Fuxian Liu
Keyword(s):  
High Resolution ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
Saliency Detection ◽  
Feature Representation ◽  
Aerial Image ◽  
Scene Classification ◽  
Representation Method ◽  
Learning Machine ◽  
Elm Classifier

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references.

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