Complex Contourlet-CNN for polarimetric SAR image classification

Li, Lingling; Ma, Liyuan; Jiao, Licheng; Liu, Fang; Sun, Quansen; Zhao, Jin

doi:10.1016/j.patcog.2019.107110

Cited by 56 publications

(18 citation statements)

References 28 publications

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“…Subfigures (j) and (k) represent the classification results of DSCNN (accuracy = 97.09%) and DSCNN followed by spatial regularization (accuracy = 97.53%), which achieve higher accuracy than the other methods. approaches for classification using PolSAR images can be found in [84], [85], [86]. Different from the previously mentioned works, which exploit the complex-valued nature of SAR images in PolSAR image classification, Huang et al [87] has recently proposed a novel deep learning framework called Deep SAR-Net for land use classification focusing on feature extraction from single-pol complex SAR images.…”

Section: A Terrain Surface Classificationmentioning

confidence: 99%

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

Zhu

Montazeri

Ali

et al. 2021

IEEE Geosci. Remote Sens. Mag.

215

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in Synthetic Aperture Radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. In this paper, we provide an introduction to the most relevant deep learning models and concepts, point out possible pitfalls by analyzing special characteristics of SAR data, review the state-of-the-art of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.

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Section: A Terrain Surface Classificationmentioning

confidence: 99%

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

Zhu

Montazeri

Ali

et al. 2021

IEEE Geosci. Remote Sens. Mag.

215

View full text Add to dashboard Cite

show abstract

“…In the deep feature extraction stage, the CapsNet still uses a single convolutional layer as the feature extractor. CNNs are widely used in the PolSAR image classification, which can extract discriminative spatial features and achieve excellent classification accuracy [46]. A convolutional operation can be defined as [47]…”

Section: Construction Of the Primary Capsule Layermentioning

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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“…With the development of DNNs, wavelet transform has several attempts to combine the classical signal processing and deep learning methods, such as image denoising [20,31,47], super resolution [16,30], classification [7,25,29], segmentation [24], facial aging [32], style transfer [50], remote sensing image processing [9], etc. It is often used as the tool of data preprocessing, post-processing, feature extraction, and sampling operators in DNNs [16,32,39,48,30,23]. [25] utilizes DWT to replace max-pooling, strided-convolution, and averagepooling to suppress the noise effect.…”

Section: Waveletsmentioning

confidence: 99%

MWQ: Multiscale Wavelet Quantized Neural Networks

Sun¹,

Ren²,

Jiao³

et al. 2021

Preprint

Self Cite

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Model quantization can reduce the model size and computational latency, it has become an essential technique for the deployment of deep neural networks on resourceconstrained hardware (e.g., mobile phones and embedded devices). The existing quantization methods mainly consider the numerical elements of the weights and activation values, ignoring the relationship between elements. The decline of representation ability and information loss usually lead to the performance degradation. Inspired by the characteristics of images in the frequency domain, we propose a novel multiscale wavelet quantization (MWQ) method. This method decomposes original data into multiscale frequency components by wavelet transform, and then quantizes the components of different scales, respectively. It exploits the multiscale frequency and spatial information to alleviate the information loss caused by quantization in the spatial domain. Because of the flexibility of MWQ, we demonstrate three applications (e.g., model compression, quantized network optimization, and information enhancement) on the ImageNet and COCO datasets. Experimental results show that our method has stronger representation ability and can play an effective role in quantized neural networks.

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Complex Contourlet-CNN for polarimetric SAR image classification

Cited by 56 publications

References 28 publications

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network

MWQ: Multiscale Wavelet Quantized Neural Networks

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