No abstract

Deep learning algorithms have been widely applied to the recognition of remote-sensing images due to their excellent performance on various recognition problems with sufficient data. However, limited data on synthetic aperture radar (SAR) images degrade the performance of neural networks for SAR automatic target recognition (ATR). To address this problem, this paper presents a new deep feature fusion framework by combining the Gabor features and information of raw SAR images and fusing the feature vectors extracted from different layers in our proposed neural network. Gabor features improve the richness of SAR image features. The number of free parameters of neural networks is largely reduced by utilizing large-scale convolutional kernel factorization and global average pooling. Moreover, the fusion of feature vectors from different layers helps improve the recognition performance of neural networks. Experimental results on the MSTAR dataset demonstrate the effectiveness of our proposed method. The proposed neural network can achieve an average accuracy of 99% on the classification of ten-class targets and even achieve a high recognition accuracy on limited data and noisy data. INDEX TERMS Automatic target recognition, deep convolutional networks, synthetic aperture radar, deep feature fusion, Gabor features.

Retrieving information concerning the interior of the ocean using satellite remote sensing data has a major impact on studies of ocean dynamic and climate changes; however, the lack of information within the ocean limits such studies about the global ocean. In this paper, an artificial neural network, combined with satellite data and gridded Argo product, is used to estimate the ocean heat content (OHC) anomalies over four different depths down to 2000 m covering the near-global ocean, excluding the polar regions. Our method allows for the temporal hindcast of the OHC to other periods beyond the 2005–2018 training period. By applying an ensemble technique, the hindcasting uncertainty could also be estimated by using different 9-year periods for training and then calculating the standard deviation across six ensemble members. This new OHC product is called the Ocean Projection and Extension neural Network (OPEN) product. The accuracy of the product is accessed using the coefficient of determination (R2) and the relative root-mean-square error (RRMSE). The feature combinations and network architecture are optimized via a series of experiments. Overall, intercomparison with several routinely analyzed OHC products shows that the OPEN OHC has an R2 larger than 0.95 and an RRMSE of <0.20 and presents notably accurate trends and variabilities. The OPEN product can therefore provide a valuable complement for studies of global climate changes.

Abstract-A comprehensive facet model for bistatic synthetic aperture radar (Bis-SAR) imagery of dynamic ocean scene is presented in this paper. An efficient facet scattering model is developed to calculate the radar cross section (RCS) of the ocean surface for Bis-SAR firstly. Further more, this facet model is combined with a bistatic velocity bunching (V B) modulation of long ocean waves to obtain the Bis-SAR intensity expression in image plane of ocean scene. The displacement of the scatter elements in the image plane and the degradation of radar resolution in azimuth direction are quantificationally analyzed. Finally, Bis-SAR imagery simulations of ocean surface are illustrated, proving the validity and practicability of the presented algorithms.