ELM-based hyperspectral imagery processor for onboard real-time classification

Basterretxea, Koldo; Martinez-Corral, Unai; Finker, Raul; Campo, I. del

doi:10.1109/dasip.2016.7853795

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Liang et al (2021) proposed the SVM classification algorithm to process postearthquake environmental images, and compared the road and village masks in remote sensing images before the earthquake to accurately identify landslides. Basterretxea et al (2016) successfully applied extreme learning machine (ELM) to terrain feature classification of high-dimensional data and designed a related hardware processor. Liang et al (2018) compared the classification performance of probabilistic neural network (PNN) and back propagation neural network (BP).…”

Section: Introductionmentioning

confidence: 99%

Research on DEM geomorphic factor terrain recognition algorithm using probabilistic neural networks based on tactile systems

Xing,

Du,

2024

Transactions of the Institute of Measurement and Control

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The accurate recognition of unknown terrain by robots plays a vital role in completing walking tasks. Haptic-based terrain recognition methods have their own advantages over visual, audio, and vibration-based terrain recognition methods. With the increasing complexity of robot application scenarios, there are still many technical difficulties in using tactile systems to recognize terrain. For real-time terrain detection technology, how to improve the accuracy of terrain recognition and shorten recognition time based on tactile systems is an urgent problem to be solved. In this paper, the digital elevation model (DEM) geomorphic factor terrain recognition algorithm using probabilistic neural networks (DGFTRA-PNN) based on tactile force-feedback system is proposed to solve this problem. First, the tactile force-feedback system is mounted on the foot ends of the hexapod robot to collect terrain data. Second, the feasibility of using DEM geomorphic factors as terrain features to identify terrain was demonstrated. Third, DGFTRA-PNNs are established based on the different walking gait of the hexapod robot, achieving terrain recognition based on DEM feature values. Finally, the accuracy and speed of DGFTRA-PNN were verified through field experiments. Compared with the terrain recognition algorithm based on back propagation neural network (BP) and extreme learning machine (ELM) algorithm, the DGFTRA-PNN has advantages in terms of terrain recognition accuracy and recognition time: the average recognition accuracy for various types of terrain is 96%, and the calculation time is 0.04 seconds.

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Section: Introductionmentioning

confidence: 99%

Research on DEM geomorphic factor terrain recognition algorithm using probabilistic neural networks based on tactile systems

Xing,

Du,

2024

Transactions of the Institute of Measurement and Control

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“…The results of this study indicated that the model established using a nonlinear data mining algorithm could achieve a better prediction effect. The prediction model based on the extreme learning machine (ELM) algorithm has strong analytical ability and robustness for nonlinear problems and has been successfully applied in many fields [8,9]. In soil detection, ELM has also been widely used to estimate soil moisture, soil temperature, and soil organic matter, for which it has achieved high prediction accuracy [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine

Jia

2019

Sensors

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Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900–1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE–ELM model performed better, having a correlation coefficient of prediction (rp), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE–ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization.

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Hyperspectral Image Classification Based on Stacked Extreme Learning Machine

Qin

2022

Lecture Notes in Electrical Engineering

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ELM-based hyperspectral imagery processor for onboard real-time classification

Cited by 7 publications

References 23 publications

Research on DEM geomorphic factor terrain recognition algorithm using probabilistic neural networks based on tactile systems

Research on DEM geomorphic factor terrain recognition algorithm using probabilistic neural networks based on tactile systems

Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine

Hyperspectral Image Classification Based on Stacked Extreme Learning Machine

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