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Due to the requirements of portability and omni-directivity, the planar bow-tie antenna is widely used in impulse through-wall radar (ITWR). When the planar bow-tie antenna is used to radiate the ultrawide bandwidth (UWB) signal, the ringing phenomenon of the transmitted signal would be serious, which can damage the quality of radar imaging. The previous solutions for this problem are the usage of various hardware loadings; however, those loadings could cause signal energy loss and reduce the signal gain. Alternatively, this letter studies a deconvolution-technique-based signal processing approach to suppress the signal ringing. Because the proposed approach does not require any hardware loadings on the antenna, it can help improve the signal-to-noise ratio (SNR) and significantly reduce the energy loss of signal. The effectivity of this signal processing approach is verified by the radar detecting experiments. [ABSTRACT FROM AUTHOR]

The extraction of joint spatial–spectral features has been proved to improve the classification performance of hyperspectral images (HSIs). Recently, utilizing convolutional neural networks (CNNs) to learn joint spatial–spectral features has become of great interest. However, the existing CNN models ignore complementary spatial–spectral information among the shallow and deep layers. Moreover, insufficient training samples in HSIs afflict these CNN models with overfitting problem. In order to address these problems, a novel CNN method for HSI classification is proposed. It considers multilayer spatial–spectral feature fusion and sample augmentation with local and nonlocal constraints, which is abbreviated as MSLN-CNN. In MSLN-CNN, a triple-architecture CNN is constructed to extract spatial–spectral features by cascading spectral features to dual-scale spatial features from shallow to deep layers. Then, multilayer spatial–spectral features are fused to learn complementary information among the shallow layers with detailed information and the deep layers with semantic information. Finally, the multilayer spatial–spectral feature fusion and classification are integrated into a unified network, and MSLN-CNN can be optimized in the end-to-end way. To alleviate the small sample size problem, the unlabeled samples having high confidences on local spatial constraint and nonlocal spectral constraint are selected and prelabeled. The nonlocal spectral constraint considers the structure information with spectrally similar samples in the nonlocal searching, while the local spatial constraint utilizes the contextual information with spatially adjacent samples. Experimental results on several hyperspectral datasets demonstrate that the proposed method achieves more encouraging classification performance than the current state-of-the-art classification methods, especially with the limited training samples. [ABSTRACT FROM AUTHOR]

Stacked autoencoder (SAE) provides excellent performance for image processing under sufficient training samples. However, the collection of training samples is difficult in hyperspectral images. Insufficient training samples easily make SAE overfit and limit the application of SAE to hypersepctral images. To address this problem, a novel marginal SAE with adaptively-spatial regularization (ARMSAE) is proposed for hyperspectral image classification. First, a superpixel segmentation method is used to divide the image into many homogenous regions. Then, at the pretraining stage, an adaptively-shaped spatial regularization is introduced to extract contextual information of samples in the homogenous regions. It sufficiently utilizes unlabeled adjacent samples to alleviate the lack of training samples. At the fine-tuning stage, the marginal samples based on geometrical property are selected to tune the ARMSAE network. The fine-tuning exploits margin strategy to alleviate the inaccurate statistical estimation caused by insufficient training samples. Finally, the label of each test sample is determined by all the samples locating in the same homogenous region. Experimental results on hyperspectral images demonstrate the proposed method provides encouraging classification performance compared with several related state-of-the-art methods. [ABSTRACT FROM AUTHOR]