Feature Selection for Cross-Scene Hyperspectral Image Classification via Improved Ant Colony Optimization Algorithm

Yu, Youhua; Xie, Xiaolan; Tang, Yigang; Liu, Yarong

doi:10.1109/access.2022.3199871

Cited by 4 publications

(3 citation statements)

References 49 publications

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“…13,17,23 Additionally, complex correlations between excessive indicators can cause Hughes phenomena, resulting in reduced model performance and even overfitting. 24 Therefore, removing redundant water quality indicators can improve model accuracy and reduce the cost of water quality monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…These indicators are called redundant indicators. Previous studies had already demonstrated that removing data on a portion of water quality indicators has no significant effect on the overall water quality assessment results. ,, Additionally, complex correlations between excessive indicators can cause Hughes phenomena, resulting in reduced model performance and even overfitting . Therefore, removing redundant water quality indicators can improve model accuracy and reduce the cost of water quality monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning-Based Approach To Assessing Water Quality in a Specific Basin: The Case of Wujingang Basin

Zhang,

He,

Wang

et al. 2023

ACS EST Water

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Multidimensional indicators of surface water are key to assessing the water quality. Cost and time could be saved if surface water can be accurately assessed by fewer indicators. Therefore, it is necessary to screen key water quality indicators for different basins. This study collected 35 water quality indicators (42 315 observations) along the Wujingang basin. Cluster analysis and correlation coefficients were used to identify homogeneous categories of water quality indicators. Frequent pattern mining (FPM) was used to remove redundant indicators. Finally, the water quality assessment after the removal of redundant indicators was validated by classification analysis. Results of the silhouette coefficient and within-cluster sum of squared errors indicated that K-means was the optimal clustering model. Concomitant indicators Pb, Cl − , NO 3− , TN, V, and Al were identified using FPM. Decision tree verified that the performance did not decrease after removing Pb, Cl − , V, NO 3 − −N, and Al. These indicators were redundant for the Wujingang basin and could be monitored less frequently when there is no special use. This study provides important information for developing a selection framework based on multidimensional water quality data, which could serve as a baseline system for the selection of key water quality indicators in a specific basin.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Approach To Assessing Water Quality in a Specific Basin: The Case of Wujingang Basin

Zhang,

He,

Wang

et al. 2023

ACS EST Water

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show abstract

“…To compensate for the band redundancy in hyperspectral images, scholars have proposed and improved a large number of spectral feature extraction methods; for example, traditional dimensionality reduction methods, such as the LASSO algorithm [24,25], the successive projections algorithm [26,27], the wavelet transform [28,29], principal component analysis [30,31], and artificial intelligence feature extraction algorithms such as the particle swarm optimization algorithm [32,33], ant colony optimization algorithm [34,35], and artificial bee colony algorithm [36,37]. However, the above methods still face challenges such as local optimality, dimensional disaster, small data sample size, and high computational complexity, because they cannot effectively identify and retain features with low individual value but high combined value.…”

Section: Introductionmentioning

confidence: 99%

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Lin

Jiang

et al. 2023

Remote Sensing

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Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field.

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A Cross-Scene Self-Representative Network for Hyperspectral Band Selection

Sun,

He,

Yang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Feature Selection for Cross-Scene Hyperspectral Image Classification via Improved Ant Colony Optimization Algorithm

Cited by 4 publications

References 49 publications

Machine-Learning-Based Approach To Assessing Water Quality in a Specific Basin: The Case of Wujingang Basin

Machine-Learning-Based Approach To Assessing Water Quality in a Specific Basin: The Case of Wujingang Basin

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

A Cross-Scene Self-Representative Network for Hyperspectral Band Selection

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