An Improved Ant Colony Algorithm for Optimized Band Selection of Hyperspectral Remotely Sensed Imagery

Ding, Xiaohui; Li, Huapeng; Ji, Yang; Dale, Patricia Ellen; Xiang-cong, Chen; Jiang, Chun‐Lei; Zhang, Shuqing

doi:10.1109/access.2020.2971327

Cited by 19 publications

(14 citation statements)

References 41 publications

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“…Ding et al [22] suggested IMCA-BS (Improved AC algorithm-based band selection) algorithm in order to solve the drawbacks of the conventional AC algorithm. In the IMACA-BS, the initialization of pheromone on each path is based on the heuristic desirability amongst each pair of nodes with the means of a pre-filter.…”

Section: Improvement By Applying Pheromones Updatementioning

confidence: 99%

Recent research in finding the optimal path by ant colony optimization

Sari

Mohamad

2021

Bulletin EEI

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The computation of the optimal path is one of the critical problems in graph theory. It has been utilized in various practical ranges of real world applications including image processing, file carving and classification problem. Numerous techniques have been proposed in finding optimal path solutions including using ant colony optimization (ACO). This is a nature-inspired metaheuristic algorithm, which is inspired by the foraging behavior of ants in nature. Thus, this paper study the improvement made by many researchers on ACO in finding optimal path solution. Finally, this paper also identifies the recent trends and explores potential future research directions in file carving.

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Section: Improvement By Applying Pheromones Updatementioning

confidence: 99%

Recent research in finding the optimal path by ant colony optimization

Sari

Mohamad

2021

Bulletin EEI

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“…Feature extraction techniques (e.g., Principal Component Analysis and Linear Discriminant Analysis) are designed to compress data by using mathematical transformations. Due to every band of the HSI having its corresponding image, the way of feature extraction that the high-dimensional feature space is mapped to a low-dimensional space by linear or nonlinear transformation could not keep the primitive physical significance of the HSI [9]. Thus, feature extraction techniques are not suitable for the dimensionality reduction of HSIs, and so FS has been one of the effective means for solving this issue on HSIs [10], specifically by selecting the optimal subset from all the original bands to gain the desired classification performance.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Swarm Intelligence and Evolutionary Algorithms for Feature Selection in SVM-Based Hyperspectral Image Classification

Shang

Zheng

et al. 2022

Remote Sensing

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Feature selection (FS) is vital in hyperspectral image (HSI) classification, it is an NP-hard problem, and Swarm Intelligence and Evolutionary Algorithms (SIEAs) have been proved effective in solving it. However, the high dimensionality of HSIs still leads to the inefficient operation of SIEAs. In addition, many SIEAs exist, but few studies have conducted a comparative analysis of them for HSI FS. Thus, our study has two goals: (1) to propose a new filter–wrapper (F–W) framework that can improve the SIEAs’ performance; and (2) to apply ten SIEAs under the F–W framework (F–W–SIEAs) to optimize the support vector machine (SVM) and compare their performance concerning five aspects, namely the accuracy, the number of selected bands, the convergence rate, and the relative runtime. Based on three HSIs (i.e., Indian Pines, Salinas, and Kennedy Space Center (KSC)), we demonstrate how the proposed framework helps improve these SIEAs’ performances. The five aspects of the ten algorithms are different, but some have similar optimization capacities. On average, the F–W–Genetic Algorithm (F–W–GA) and F–W–Grey Wolf Optimizer (F–W–GWO) have the strongest optimization abilities, while the F–W–GWO requires the least runtime among the ten. The F–W–Marine Predators Algorithm (F–W–MPA) is second only to the two and slightly better than F–W–Differential Evolution (F–W–DE). The F–W–Ant Lion Optimizer (F–W–ALO), F–W–I-Ching Divination Evolutionary Algorithm (F–W–IDEA), and F–W–Whale Optimization Algorithm (F–W–WOA) have the middle optimization abilities, and F–W–IDEA takes the most runtime. Moreover, the F–W–SIEAs outperform other commonly used FS techniques in accuracy overall, especially in complex scenes.

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“…Therefore, the information provided by HSI is sufficient for the extraction of detailed land-use information. However, the massive bands often lead to the curse of dimensionality [4]. The classification performances of the commonly used classifiers (e.g., support vector machine (SVM) and random forest (RF)) for HSI were often limited without the reduction of feature dimensionality (e.g., feature extraction and feature selection).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Capsule Network for Hyperspectral Remote Sensing Classification

Ding

Yang

et al. 2021

Remote Sensing

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The capsule network (Caps) is a novel type of neural network that has great potential for the classification of hyperspectral remote sensing. However, the Caps suffers from the issue of gradient vanishing. To solve this problem, a powered activation regularization based adaptive capsule network (PAR-ACaps) was proposed for hyperspectral remote sensing classification, in which an adaptive routing algorithm without iteration was applied to amplify the gradient, and the powered activation regularization method was used to learn the sparser and more discriminative representation. The classification performance of PAR-ACaps was evaluated using two public hyperspectral remote sensing datasets, i.e., the Pavia University (PU) and Salinas (SA) datasets. The average overall classification accuracy (OA) of PAR-ACaps with shallower architecture was measured and compared with those of the benchmarks, including random forest (RF), support vector machine (SVM), 1-dimensional convolutional neural network (1DCNN), two-dimensional convolutional neural network (CNN), three-dimensional convolutional neural network (3DCNN), Caps, and the original adaptive capsule network (ACaps) with comparable network architectures. The OA of PAR-ACaps for PU and SA datasets was 99.51% and 94.52%, respectively, which was higher than those of benchmarks. Moreover, the classification performance of PAR-ACaps with relatively deeper neural architecture (four and six convolutional layers in the feature extraction stage) was also evaluated to demonstrate the effectiveness of gradient amplification. As shown in the experimental results, the classification performance of PAR-ACaps with relatively deeper neural architecture for PU and SA datasets was also superior to 1DCNN, CNN, 3DCNN, Caps, and ACaps with comparable neural architectures. Additionally, the training time consumed by PAR-ACaps was significantly lower than that of Caps. The proposed PAR-ACaps is, therefore, recommended as an effective alternative for hyperspectral remote sensing classification.

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An Improved Ant Colony Algorithm for Optimized Band Selection of Hyperspectral Remotely Sensed Imagery

Cited by 19 publications

References 41 publications

Recent research in finding the optimal path by ant colony optimization

Recent research in finding the optimal path by ant colony optimization

A Comparative Analysis of Swarm Intelligence and Evolutionary Algorithms for Feature Selection in SVM-Based Hyperspectral Image Classification

An Adaptive Capsule Network for Hyperspectral Remote Sensing Classification

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