Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image

As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because ELM does not use the spatial information which is very important for HSI classification. In view of this, this paper proposes a new framework for spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based on lots of experiments and analysis, we found out that the LELM is a better choice than nonlinear ELM for spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learn such distribution using the LBP. The proposed method not only maintain the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines and Pavia University, demonstrate the good performances of the proposed method.Comment: 13 pages,8 figures,3 tables,articl

Are Microphone Signals Alone Sufficient for Joint Microphones and Sources Localization?

Joint microphones and sources localization can be achieved by using both time of arrival (TOA) and time difference of arrival (TDOA) measurements, even in scenarios where both microphones and sources are asynchronous due to unknown emission time of human voices or sources and unknown recording start time of independent microphones. However, TOA measurements require both microphone signals and the waveform of source signals while TDOA measurements can be obtained using microphone signals alone. In this letter, we explore the sufficiency of using only microphone signals for joint microphones and sources localization by presenting two mapping functions for both TOA and TDOA formulas. Our proposed mapping functions demonstrate that the transformations of TOA and TDOA formulas can be the same, indicating that microphone signals alone are sufficient for joint microphones and sources localization without knowledge of the waveform of source signals. We have validated our proposed mapping functions through both mathematical proof and experimental results.Comment: 2 figure

Convolutional neural network extreme learning machine for effective classification of hyperspectral images

Due to its excellent performance in terms of fast implementation, strong generalization capability and straightforward solution, extreme learning machine (ELM) has attracted increasingly attentions in pattern recognition such as face recognition and hyperspectral image (HSI) classification. However, the performance of ELM for HSI classification remains a challenging problem especially in effective extraction of the featured information from the massive volume of data. To this end, we propose in this paper a new method to combine Convolutional neural network (CNN) with ELM (CNN-ELM) for HSI classification. As CNN has been successfully applied for feature extraction in different applications, the combined CNN-ELM approach aims to take advantages of these two techniques for improved classification of HSI. By preserving the spatial features whilst reconstructing the spectral features of HSI, the proposed CNN-ELM method can significantly improve the accuracy of HSI classification without increasing the computational complexity. Comprehensive experiments using three publicly available HSI data sets, Pavia University, Pavia center, and Salinas have fully validated the improved performance of the proposed method when benchmarking with several state-of-the-art approaches

Locality Regularized Robust-PCRC: A Novel Simultaneous Feature Extraction and Classification Framework for Hyperspectral Images

Despite the successful applications of probabilistic collaborative representation classification (PCRC) in pattern classification, it still suffers from two challenges when being applied on hyperspectral images (HSIs) classification: 1) ineffective feature extraction in HSIs under noisy situation; and 2) lack of prior information for HSIs classification. To tackle the first problem existed in PCRC, we impose the sparse representation to PCRC, i.e., to replace the 2-norm with 1-norm for effective feature extraction under noisy condition. In order to utilize the prior information in HSIs, we first introduce the Euclidean distance (ED) between the training samples and the testing samples for the PCRC to improve the performance of PCRC. Then, we bring the coordinate information (CI) of the HSIs into the proposed model, which finally leads to the proposed locality regularized robust PCRC (LRR-PCRC). Experimental results show the proposed LRR-PCRC outperformed PCRC and other state-of-the-art pattern recognition and machine learning algorithms

Low Rank Properties for Estimating Microphones Start Time and Sources Emission Time

The absence of unknown timing information about the microphones recording start time and the sources emission time presents a challenge in several applications, including joint microphones and sources localization. Compared with traditional optimization methods that try to estimate unknown timing information directly, low rank property (LRP) contains an additional low rank structure that facilitates a linear constraint of unknown timing information for formulating corresponding low rank structure information, enabling the achievement of global optimal solutions of unknown timing information with suitable initialization. However, the initialization of unknown timing information is random, resulting in local minimal values for estimation of the unknown timing information. In this paper, we propose a combined low rank approximation method to alleviate the effect of random initialization on the estimation of unknown timing information. We define three new variants of LRP supported by proof that allows unknown timing information to benefit from more low rank structure information. Then, by utilizing the low rank structure information from both LRP and proposed variants of LRP, four linear constraints of unknown timing information are presented. Finally, we use the proposed combined low rank approximation algorithm to obtain global optimal solutions of unknown timing information through the four available linear constraints. Experimental results demonstrate superior performance of our method compared to state-of-the-art approaches in terms of recovery rate (the number of successful initialization for any configuration), convergency rate (the number of successfully recovered configurations), and estimation errors of unknown timing information.Comment: 13 pages for main content; 9 pages for proof of proposed low rank properties; 13 figure

Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images.

As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because ELM does not use spatial information, which is very important for HSI classification. In view of this, this paper proposes a new framework for the spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are an improvement of linear ELM (LELM). However, based on lots of experiments and much analysis, it is found that the LELM is a better choice than nonlinear ELM for the spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learns such a distribution using the LBP. The proposed method not only maintains the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines, and Pavia University, demonstrate the good performance of the proposed method

Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images.

Although extreme learning machines (ELM) have been successfully applied for the classification of hyperspectral images (HSIs), they still suffer from three main drawbacks. These include: 1) ineffective feature extraction (FE) in HSIs due to a single hidden layer neuron network used; 2) ill-posed problems caused by the random input weights and biases; and 3) lack of spatial information for HSIs classification. To tackle the first problem, we construct a multilayer ELM for effective FE from HSIs. The sparse representation is adopted with the multilayer ELM to tackle the ill-posed problem of ELM, which can be solved by the alternative direction method of multipliers. This has resulted in the proposed multilayer sparse ELM (MSELM) model. Considering that the neighboring pixels are more likely from the same class, a local block extension is introduced for MSELM to extract the local spatial information, leading to the local block MSELM (LBMSELM). The loopy belief propagation is also applied to the proposed MSELM and LBMSELM approaches to further utilize the rich spectral and spatial information for improving the classification. Experimental results show that the proposed methods have outperformed the ELM and other state-of-the-art approaches