Multiobjective evolutionary algorithm assisted stacked autoencoder for PolSAR image classification

Liu, Guangyuan; Li, Yangyang; Jiao, Licheng; Chen, Yanqiao; Shang, Ronghua

doi:10.1016/j.swevo.2020.100794

Cited by 27 publications

(11 citation statements)

References 36 publications

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“…SSAE-LSI [9], CV-CNN [12], PFDCN [13], DSNet [14], PolMPCNN [15], FCN [19] and CV-FCN [20] belong to handcrafted models. SAE-MOEA/D [23], PDAS [24] and CV-PDAS [24] are auto-design models.…”

Section: Comparison Experiments and Resultsmentioning

confidence: 99%

“…Also, we still need to determine the input features. Thirdly, although neural architecture search (NAS) [22], which aims to automatically search and design architectures, has been proposed recently and has good performances on many tasks, only a few NAS methods [23], [24] in PolSAR image classification are proposed. Furthermore, the improvements brought by these NAS methods in PolSAR image classification are limited.…”

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confidence: 99%

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Pol-NAS: A Neural Architecture Search Method With Feature Selection for PolSAR Image Classification

Liu

Chen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the development of deep learning, more and more neural networks have been used in polarimetric synthetic aperture radar (PolSAR) image classification and obtain good results. As we all know, the performances of neural networks highly depend on well-designed neural architectures. Besides, the features input to neural networks also have a huge impact on the classification results. Both architecture design and feature selection are time-consuming and require human expertise. So, in this paper, we propose a neural architecture search method with feature selection (Pol-NAS) for PolSAR image classification. It can automatically search and obtain a good architecture including intra-cell, inter-cell structure and the number of layers in the search stage. Meanwhile, all the features commonly used in PolSAR data interpretation, rather than part of them, are input to the model in order to avoid selecting the size of optimal feature subset, which is a hyper-parameter and usually different for different models. Then, we propose the Feature Attention block (FA block) and redesign the stem layers by combining the FA block and the original stem layers. Thus, Pol-NAS can adaptively find the importance of each feature in the training stage by using the redesigned stem layers. With the help of Pol-NAS, we only need to prepare the data and wait for the classification results. Experimental results on three real PolSAR datasets show that the performance of Pol-NAS is better than that of state-of-theart PolSAR image classification models. The code is available at https://github.com/guangyuanLiu/Pol-NAS.

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Section: Comparison Experiments and Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Pol-NAS: A Neural Architecture Search Method With Feature Selection for PolSAR Image Classification

Liu

Chen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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“…Thus, hidden encoded implementation of existing AE is considered as input of upcoming AE. When the pre-training layers of SAE have been completed, the decoder for an AE has been dropped [21]. This is followed by, connect the encoders as well as fine-tune weights of SAE BY using softmax regression.…”

Section: Ssa-sae Based Predictive Model For Fcpmentioning

confidence: 99%

Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

Muneer¹,

Taib²,

Fati³

et al. 2022

Computers, Materials &Amp; Continua

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Recently, Financial Technology (FinTech) has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm. Financial crisis prediction (FCP) is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution. At the same time, the development of the internet of things (IoT) has altered the mode of human interaction with the physical world. The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process. This paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder (MOSSA-SAE) model for FCP in IoT environment. The MOSSA-SAE model encompasses different subprocesses namely preprocessing, class imbalance handling, parameter tuning, and classification. Primarily, the MOSSA-SAE model allows the IoT devices such as smartphones, laptops, etc., to collect the financial details of the users which are then transmitted to the cloud for further analysis. In addition, SMOTE technique is employed to handle class imbalance problems. The goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of SMOTE. Besides, SAE model is utilized as a classification technique to determine the class label of the financial data. At the same time, the MOSSA is applied to appropriately select the 'weights' and 'bias' values of the SAE. An extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct aspects. The experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.

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“…As the diagonal elements of the [T 3 ], they contain comprehensive amplitude information and can be directly or indirectly used for PolSAR image classification [43,44]. Moreover, the last one consists of Entropy (H), Anisotropy (A), and Scattering angle (α), which are commonly used to describe the scattering mechanisms [45]. As the change intervals of these features are of different orders of magnitude, we adopt a normalization operation to map the features to the range of 0 to 1.…”

Section: Polarimetric Feature Extractionmentioning

confidence: 99%

PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network

et al. 2021

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Polarimetric synthetic aperture radar (PolSAR) image classification is one of the basic methods of PolSAR image interpretation. Deep learning algorithms, especially convolutional neural networks (CNNs), have been widely used in PolSAR image classification due to their powerful feature learning capabilities. However, a single neuron in the CNN cannot represent multiple polarimetric attributes of the land cover. The capsule network (CapsNet) uses vectors instead of the single neuron to characterize the polarimetric attributes, which improves the classification performance compared with traditional CNNs. In this paper, a hierarchical capsule network (HCapsNet) is proposed for the land cover classification of PolSAR images, which can consider the deep features obtained at different network levels in the classification. Moreover, we adopt three attributes to uniformly describe the scattering mechanisms of different land covers: phase, amplitude, and polarimetric decomposition parameters, which improves the generalization performance of HCapsNet. Furthermore, conditional random field (CRF) is added to the classification framework to eliminate small isolated regions of the intra-class. Comprehensive evaluations are performed on three PolSAR datasets acquired by different sensors, which demonstrate that our proposed method outperforms other state-of-the-art methods.

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Multiobjective evolutionary algorithm assisted stacked autoencoder for PolSAR image classification

Cited by 27 publications

References 36 publications

Pol-NAS: A Neural Architecture Search Method With Feature Selection for PolSAR Image Classification

Pol-NAS: A Neural Architecture Search Method With Feature Selection for PolSAR Image Classification

Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network

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