As a typical pattern recognition problem, specific emitter identification (SEI) is a crucial step to achieve efficient spectrum sensing. In this work, an emitter identification method based on Signal Graph Capsule Network, which refered as SGCN, is proposed. First, emitter signal is transformed into an undirected graph according to the Euclidean distance from its sampling point, and then take the undirected graph as the input of the network. Second, optimizing the topological structural characteristics by graph convolution operation on this undirected graph. Finally, by introduce the capsule network to improve the generalization ability and enhance the robustness. Extensive analysis and experiments on 30 individual emitters signals demonstrates the attentiveness of the proposed model.
As an important mission of communication reconnaissance field, the action recognition of radio signals has attracted great attention of researchers, where feature extraction of signals is considered as the most essential element. However, feature extraction is still a challenging task in action recognition owing to the remarkable distortions and rapid changes of signals. In this paper, a novel deep learning method is proposed for simultaneous feature extraction and action recognition of signals, which can automatically learn discriminative and robust features layer by layer in a supervised manner. Firstly, signals are sliced and stacked into a real-valued matrix, and then fed into a multi-scale deep convolution neural network. Unlike the same size kernels employed in traditional convolution networks, multi-scale convolution kernels are advanced to explore diverse spatial correlation of signal matrix. In each layer, multiple one-dimensional (1-D) convolution kernels are designed to filter out representative features. Then 1-D pooling operators are constructed to reduce the dimensionality of features to formulate the multi-scale feature maps. Finally a softmax classifier is used to recognize the types of actions. By learning a group of 1-D filters from a large number of labeled signals, our proposed deep convolutional network can automatically extract the fine signatures of signals and explore the latent difference between communication signals and non-communication signals. Moreover, the 1-D convolution is of low-complexity computation and can simplify the computation during the backpropagation of the neural network. Experimental results on real signals show that our proposed method is effective in recognizing communication signals and noncommunication signals.
Markov random field (MRF) model is an effective tool for polarimetric synthetic aperture radar (PolSAR) image classification. However, due to the lack of suitable contextual information in conventional MRF methods, there is usually a contradiction between edge preservation and region homogeneity in the classification result. To preserve edge details and obtain homogeneous regions simultaneously, an adaptive MRF framework is proposed based on a polarimetric sketch map. The polarimetric sketch map can provide the edge positions and edge directions in detail, which can guide the selection of neighborhood structures. Specifically, the polarimetric sketch map is extracted to partition a PolSAR image into structural and nonstructural parts, and then adaptive neighborhoods are learned for two parts. For structural areas, geometric weighted neighborhood structures are constructed to preserve image details. For nonstructural areas, the maximum homogeneous regions are obtained to improve the region homogeneity. Experiments are taken on both the simulated and real PolSAR data, and the experimental results illustrate that the proposed method can obtain better performance on both region homogeneity and edge preservation than the state-of-the-art methods.
We present a novel fusion method to improve the spatial resolution of multispectral (MS) images, where the fused spectral images integrate the spectral information and spatial details from the original MS images and panchromatic (PAN) image, respectively. Band by band, a spectral image with high resolution is reconstructed from the original spectral image to take advantage of super-resolution technology. The pan-sharpening method via Amélioration de la Resolution Spatiale par Injection de Structures concept is further applied to obtain the fused images from the reconstructed spectral images and PAN image. Performance of the proposed method has been evaluated on the public optical satellite QuickBird images. Experimental results show that the fused spectral images both preserve the spatial details of high-resolution from the PAN image and have higher spectral resolution than the original spectral images.
Synthetic aperture radar (SAR) images compression is very important in reducing the burden of data storage and
transmission. Finding efficient geometric representations of images is a central issue in improving the efficiency of
image compression. Bandelet provides an efficient way for image representation based on geometric regularity. In the
second generation Bandelet, the multiscale decomposition of image is completed by 2D wavelet transform (WT) and the
obtained subbands images are squared partitioned. Then a bottom to top CART algorithm is used to prune the quadtree,
and finally an exhaustive searching algorithm is used to obtain the optimal direction in each square. This process is of
high complexity in time and space though it can provide an efficient representation of images than WT. Considering this,
we proposed a rapid implementation of Bandelet transform based on fixed size image partition, and then applied it to
SAR image compression. Experiments results show that in relative to the second generation Bandelets, our proposed
method has rapid implementation and comparable performance with chinalake and abq_apt in 0.5-2.0bpp. An
improvement of PSNR(Peak Signal to Noise Ratio) and the preservation of edges and texture over JPEG2000 are
obtained.
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