Semi-supervised Remote Sensing Image Scene Classification Based on Generative Adversarial Networks

Guo, Dongen; Wu, Zechen; Zhang, Yuanzheng; Shen, Zhen

doi:10.1007/s44196-022-00150-0

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…The performance of supervised scene classification is limited by the lack of labeled remote sensing images as compared to the field of natural images, and unsupervised methods are not as suitable for practical applications. Thus, this paper presents a generative adversarial network-based semisupervised remote sensing image scene classification technique [2].…”

Section: Introductionmentioning

confidence: 99%

Holdout based blending approaches for improved satellite image classification

Kumar Musali,

Janthakal,

Rajasekhar

2024

IJECE

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An essential component of remote sensing, image analysis, and pattern recognition is image categorization. The classification of land use using remotely sensed data creates a map-like representation as the final form of the investigation. With its ability to effectively categorize satellite images, machine learning (ML) algorithms have gained significant traction in a number of fields, including land-use planning, disaster response, and natural resource management. Ensemble learning is also a widely used technique for enhancing the precision of satellite image categorization, which combines multiple models to get more precise predictions. Holdout is an ensemble technique, where multiple ML algorithms are used for training on the same dataset. The primary goal of this study is to create a holdout model for classifying satellite images. Initially, this study explores the usage of ML algorithms namely support vector machines (SVM), k-nearest neighbor (KNN), decision trees (DT), gradient boosting classifier (GBC), histogram-based GBC (HGBC), random forest classifier (RF), bagging classifier (BC), XGBoost classifier for classifying satellite images. Later, GBC, HGBC, RF, BC, and XGBoost are combined to build a stacking model. The bagging ensemble model outperforms all other methods and reaches an accuracy of 88.90%. Finally, blending models with holdout approach were developed and achieved accuracy of 93.70%, 94.14%, and 93.87% which outperformed all previous algorithms.

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Section: Introductionmentioning

confidence: 99%

Holdout based blending approaches for improved satellite image classification

Kumar Musali,

Janthakal,

Rajasekhar

2024

IJECE

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“…Variational auto-encoder (VAE) [10] and generative adversarial network (GAN) [11] are two typical generative neural network models. The VAE model must assume a priori distribution [12], limiting its robustness. Based on the "black box" characteristics of neural networks, the GAN model can characterize the uncertainty of renewable energy output and can complete the scene generation task by constructing different forms of generator…”

Section: Introductionmentioning

confidence: 99%

Method for constructing typical operation scenarios in power systems considering spatiotemporal correlation of renewable energy generation

Bai,

Li,

Zhao

et al. 2024

J. Phys.: Conf. Ser.

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The proposed article focuses on developing a methodology for constructing typical operational scenarios in power systems considering the spatiotemporal characteristics of new energy output. The integration of new energy sources on a large scale into power systems can lead to issues such as line congestion and supply-demand imbalances. To support the planning and scheduling of future high-capacity power systems, this paper introduces a method that accounts for the spatiotemporal characteristics of new energy outputs. The method begins by proposing a spatiotemporal fusion-based multi-head attention model for generating new energy output scenarios. This model employs multi-head attention mechanisms to extract the spatiotemporal characteristics of new energy outputs, eliminating redundancy in the feature data. The processed spatiotemporal feature information is then transferred to a gated feature fusion model for integration, using an encoder structure based on Long Short-Term Memory (LSTM) to generate new energy output scenarios. The paper, by means of an advanced Fuzzy C-Means (FCM) clustering algorithm, identifies typical scenarios of net energy output. Subsequently, the proposed method’s efficacy and precision are confirmed through simulation analysis with real data from a certain area.

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Semi-supervised Remote Sensing Image Scene Classification Based on Generative Adversarial Networks

Cited by 2 publications

References 14 publications

Holdout based blending approaches for improved satellite image classification

Holdout based blending approaches for improved satellite image classification

Method for constructing typical operation scenarios in power systems considering spatiotemporal correlation of renewable energy generation

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