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Deep neural networks have achieved great progress in 3D scene understanding. However, recent methods mainly focused on objects with canonical orientations in contrast with random postures in reality. In this letter, we propose a hierarchical neural network, named Local Frame Network (LFNet), based on the local rotation invariant coordinate frame for robust point cloud analysis. The local point patches in different orientated objects are transformed into an identical distribution based on this coordinate frame, and the transformed coordinates are taken as input features to eliminate the influence of rotations at the input level. Meanwhile, a discrete convolution operator is defined in the constructed coordinate frame to extract rotation invariant features from local patches, which can further remove the influence of rotations at the convolution level. Moreover, a Spatial Feature Encoder (SFE) module is utilized to perceive the spatial structure of the local region. Mathematical analysis and experimental results on two public datasets demonstrate that the proposed method can eliminate the influence of rotations without data augmentation and outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The filtered backprojection (FBP) algorithm is a popular choice for complicated trajectory synthetic aperture radar (SAR) image formation processing due to its inherent nonlinear motion compensation capability. However, how to efficiently refocus the defocused FBP imagery when the motion measurement is not accurate enough is still a challenging problem. In this paper, a new interpretation of the FBP derivation is presented from the Fourier transform point of view. Based on this new viewpoint, the property of the residual 2-D phase error in FBP imagery is analyzed in detail. Then, by incorporating the derived a priori knowledge on the 2-D phase error, an accurate and efficient 2-D autofocus approach is proposed. This new approach performs the parameter estimation in a dimension-reduced parameter subspace by exploiting the a priori analytical structure of the 2-D phase error, therefore it possesses much higher accuracy and efficiency than the conventional blind methods. Finally, experimental results clearly demonstrate the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the ability of powerful feature representation, deep-learning-based object detection has attracted considerable research attention, and many methods have been proposed for remote sensing images. However, there are still some problems that need to be addressed. In this paper, a novel and effective detection framework based on faster region-based convolutional neural network is designed. Specifically, first, in order to locate the boundaries of large objects and find the missing small objects, DetNet is incorporated into the detection framework as the backbone network. DetNet fixes the spatial resolution in deep layers and adopts dilated bottleneck with convolution projection to increase the divergence between input and output feature maps. Then, the proposed framework uses the backbone network to extract the scene features and region features simultaneously, which are both mapped to feature vectors and then fused together. The feature fusion operation can improve the feature representation of the generated region. Last, to improve the performance of localization, the cascade structure is adopted in the framework. The cascade structure has multiple phases and every phase has independent classifier and regressor. The results obtained from the previous phase are used as the regions of interest in the next phase. Therefore, the multiphase detector can increase the detection accuracy phase by phase. Comprehensive evaluations on a public ten-class object detection dataset demonstrate the effectiveness of the proposed framework. Moreover, ablation experiments are also implemented to show the respective influence of different parts of the framework on the performance improvement. [ABSTRACT FROM AUTHOR]

In the optical region, the global scattering center (SC) model is important for target recognition and data compression. However, the existing methods are difficult to be accomplished because of the requirement for large storage or time consumption. To solve this problem, a new easy-to-implement method is proposed in this paper. By employing a modified random sample consensus method, all candidate positions for the global SC model are extracted and discriminated efficiently. The clean step is unnecessary in the proposed method, which makes the procedure more efficient. Experimental results on the high-frequency-electromagnetic data demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]