Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification

Bera, Somenath; Shrivastava, Vimal K.

doi:10.1080/01431161.2019.1694725

Cited by 184 publications

(83 citation statements)

References 37 publications

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“…The Cross-Entropy Loss, also referred to as Logarithmic Loss, is the most suitable for both binary and multi-class classification problems [26] and, thus, it was selected for the optimization process. For the training of the network of the classifier, several optimizers that are used in state-of-the-art methods [12] were experimented with, and those which achieved the best results, depicted in the following section, were the following: Adadelta, Adam, RMSprop, and SGD (Stochastic Gradient Descent).…”

Section: ) Lightweight Volumetric Cnn Architecturementioning

confidence: 99%

Advancing Autonomous Surface Vehicles: A 3D Perception System for the Recognition and Assessment of Docking-Based Structures

et al. 2021

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The automation of typically intelligent and decision-making processes in the maritime industry leads to fewer accidents and more cost-effective operations. However, there are still lots of challenges to solve until fully autonomous systems can be employed. Artificial Intelligence (AI) has played a major role in this paradigm shift and shows great potential for solving some of these challenges, such as the docking process of an autonomous vessel. This work proposes a lightweight volumetric Convolutional Neural Network (vCNN) capable of recognizing different docking-based structures using 3D data in real-time. A synthetic-to-real domain adaptation approach is also proposed to accelerate the training process of the vCNN. This approach makes it possible to greatly decrease the cost of data acquisition and the need for advanced computational resources. Extensive experiments demonstrate an accuracy of over 90% in the recognition of different docking structures, using low resolution sensors. The inference time of the system was about 120ms on average. Results obtained using a real Autonomous Surface Vehicle (ASV) demonstrated that the vCNN trained with the synthetic-to-real domain adaptation approach is suitable for maritime mobile robots. This novel AI recognition method, combined with the utilization of 3D data, contributes to an increased robustness of the docking process regarding environmental constraints, such as rain and fog, as well as insufficient lighting in nighttime operations.INDEX TERMS Autonomous surface vehicle, docking, object recognition, point cloud.

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Section: ) Lightweight Volumetric Cnn Architecturementioning

confidence: 99%

Advancing Autonomous Surface Vehicles: A 3D Perception System for the Recognition and Assessment of Docking-Based Structures

et al. 2021

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“…Adam is a random objective function optimization algorithm based on a one-step degree and also an adaptive estimation algorithm based on a low-order matrix. This method has high calculation efficiency and small memory requirements and is very suitable for optimization problems with a large data volume or many parameters [44]. The algorithm is as follows:…”

Section: Adaptive Moment Estimation Optimizer (Adam)mentioning

confidence: 99%

A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

et al. 2020

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As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

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“…x Ne ) represent a leading bee population, and X0 represent the initial leading bee population. The bee population can be initialized by Equation (1).…”

Section: Review Of Abcmentioning

confidence: 99%

“…Compared with traditional classification methods such as maximum likelihood classification, K-Means, ISODATA, and deep learning methods, non-parametric classification methods such as decision trees and support vector machines do not need to make too many assumptions about the data distribution, and have obvious advantages in solving problems such as small samples, nonlinearity, local minimums, etc. [1], [2], so they have been widely used in remote sensing data classification. In particular, the SVM algorithm does not require the assumption of normal distribution of the data, and has outstanding The associate editor coordinating the review of this manuscript and approving it for publication was Zhihan Lv . advantages in solving small sample, nonlinear, and highdimensional data classification problems, so it is more suitable for classification problems in complex surface environments [3], [4], Such as urban scenes [5]- [9], crops [10]- [12], forests [13], [14], wetlands [15], and so on.…”

Section: Introductionmentioning

confidence: 99%

Improvement SVM Classification Performance of Hyperspectral Image Using Chaotic Sequences in Artificial Bee Colony

Zhao

Wang

et al. 2020

IEEE Access

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Artificial bee colony algorithm is an effective algorithm for parameter optimization, but the traditional artificial bee colony algorithm is liable to fall into local extreme points at a later stage. In this paper, we propose an improved artificial bee colony optimization algorithm, which solves the problems of premature convergence and falling into the local extreme value in the classification of hyperspectral images. First we use an improved chaotic sequence with higher randomness to initialize and update nectar sources to expand the distribution of nectar sources. Secondly, the optimized adaptive step size is introduced into the neighborhood search to speed up the algorithm convergence and improve the search efficiency. Then we build an improved artificial bee colony algorithm support vector machine optimization model to mine the optimal values of penalty factor C and kernel function parameter σ. Next, the model was used to perform classification experiments on two hyperspectral images (University of Pavia, Indian Pine) with different attributes, and compared with the traditional bee colony algorithm, genetic algorithm, and particle swarm algorithm. Experimental results on HSI datasets demonstrate the superiority of the proposed method over several well-known methods in both classification accuracy and convergence speed. INDEX TERMS Artificial bee colony, support vector machine, hyperspectral image, chaotic sequence.

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Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification

Cited by 184 publications

References 37 publications

Advancing Autonomous Surface Vehicles: A 3D Perception System for the Recognition and Assessment of Docking-Based Structures

Advancing Autonomous Surface Vehicles: A 3D Perception System for the Recognition and Assessment of Docking-Based Structures

A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

Improvement SVM Classification Performance of Hyperspectral Image Using Chaotic Sequences in Artificial Bee Colony

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