Classification of polarimetric SAR images using compact convolutional neural networks

Ahishali, Mete; Kıranyaz, Serkan; İnce, Türker; Gabbouj, Moncef

doi:10.1080/15481603.2020.1853948

Cited by 20 publications

(5 citation statements)

References 44 publications

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“…The training sample rate in the comparison experiments is also set as 1%. Four methods are selected for comparison: the multichannel fusion convolutional neural network based on scattering mechanism (MCCNN) [52], the compact and adaptive implementation of CNNs using a sliding-window classification approach [57], the composite kernel and Hybrid discriminative random field model based on feature fusion (CK-HDRF) [58], and the recurrent complex-valued convolutional neural network (RCV-CNN) [13]. In which, CK-HDRF belongs to machine learning, RCV-CNN belongs to semi-supervised learning.…”

Section: Analysis Of the Performancementioning

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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Section: Analysis Of the Performancementioning

confidence: 99%

PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network

et al. 2021

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“…These CNN-based models have demonstrated remarkable capabilities in various classification tasks and have been extended and enhanced for PolSAR image classification 9 , 10 . Several papers and research studies have addressed similar issues and have contributed to the field, for example, semi-supervised complex-valued generative adversarial networks, 11 classification of polarimetric SAR images using compact CNNs, 12 and semi-supervised classification of PolSAR images based on co-training of CNN and SVM 13 . These advancements in research are driving progress in PolSAR image classification and opening up new avenues for more accurate and practical remote sensing applications.…”

Section: Introductionmentioning

confidence: 99%

Land cover analysis of PolSAR images using probabilistic voting ensemble and integrated support vector machine

AboElenean,

Helmy,

ElTohamy

et al. 2023

J. Appl. Rem. Sens.

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.Land cover classification is a vital application of polarimetric synthetic aperture radar (PolSAR) images in various fields, such as agriculture monitoring and urban assessment. We introduce a modified and enhanced PolSAR image classification method, combining six decomposition techniques, a support vector machine (SVM) based classifier, and a probabilistic voting ensemble (PVE) model. Our method addresses the challenges posed by the complexity of PolSAR data and the limited availability of labeled samples. The core of our approach lies in integrating multiple decomposition techniques as feature extractors, aiming to capture diverse scattering behaviors and uncover valuable information related to land cover characteristics. These techniques include the Huynen, Cloude, Freeman and Durden, HAAlpha, Yamaguchi, and Vanzyl decomposition methods. The extracted features are then utilized as inputs for training the SVM base classifier. To enhance classification performance, a PVE model is used to combine predictions from each decomposition technique, considering the individual prediction confidence and the characteristics of the decomposition methods. The decision fusion process is applied to integrate diverse predictions based on the majority voting and estimated class probability, providing a more robust and reliable final label prediction and thereby improving the overall accuracy of the classification process. Experimental analyses are conducted on airborne and spaceborne PolSAR images, covering various bands and land cover types, to evaluate the effectiveness and robustness of our proposed method. The experimental results demonstrate that our approach yields more confident class predictions than alternative methods.

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“…Compared to ML, convolutional neural networks (CNNs) are capable of analyzing the information of adjacent pixels and better extracting image features. This has resulted in better results in image classification researches [19,20], and convolutional neural networks such as fully convolutional networks (FCNs), e.g., DeepLab V3+, are consequently becoming increasingly popular in LULC and vegetation classification [21,22]. FCNs have replaced the last layer in CNNs with a deconvolution [23], which has retained the advantages of CNNs and enhanced the accuracy of image semantic segmentation independent of the input image size.…”

Section: Introductionmentioning

confidence: 99%

Mapping Vegetation Types by Different Fully Convolutional Neural Network Structures with Inadequate Training Labels in Complex Landscape Urban Areas

Chen,

Zhang,

Lei

2023

Forests

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Highly accurate urban vegetation extraction is important to supporting ecological and management planning in urban areas. However, achieving high-precision classification of urban vegetation is challenging due to dramatic land changes in cities, the complexity of land cover, and hill shading. Although convolutional neural networks (CNNs) have unique advantages in remote sensing image classification, they require a large amount of training sample data, making it difficult to adequately train the network to improve classification accuracy. Therefore, this paper proposed an urban vegetation classification method by combining the advantages of transfer learning, deep learning, and ensemble learning. First, three UNet++ networks (UNet++, VGG16-UNet++, and ResNet50-UNet++) were pre-trained using the open sample set of urban land use/land cover (LULC), and the deep features of Sentinel-2 images were extracted using the pre-trained three UNet++ networks. Subsequently, the optimal deep feature set was then selected by Relief-F and input into the Stacking algorithm for urban vegetation classification. The results showed that deeper features extracted by UNet++ networks were able to easily distinguish between different vegetation types compared to Sentinel-2 spectral features. The overall classification accuracy (OA) of UNet++ networks and the Stacking algorithm (UNS) was 92.74%, with a Kappa coefficient of 0.8905. The classification results of UNet++ networks and the Stacking algorithm improved by 2.34%, 1.8%, 2.29%, and 10.74% in OA compared to a single neural network (UNet++, VGG16-UNet++, and ResNet50-UNet++) and the Stacking algorithm, respectively. Furthermore, a comparative analysis of the method with common vegetation classification algorithms (RF, U-Net, and DeepLab V3+) indicated that the results of UNS were 11.31%, 9.38%, and 3.05% better in terms of OA, respectively. Generally, the method developed in this paper could accurately obtain urban vegetation information and provide a reference for research on urban vegetation classification.

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Classification of polarimetric SAR images using compact convolutional neural networks

Cited by 20 publications

References 44 publications

PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network

PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network

Land cover analysis of PolSAR images using probabilistic voting ensemble and integrated support vector machine

Mapping Vegetation Types by Different Fully Convolutional Neural Network Structures with Inadequate Training Labels in Complex Landscape Urban Areas

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