Deep learning methods excel in Polarimetric SAR (PolSAR) image classification. However, existing methods typically sample an image block for each pixel with a fixed-size square window, which always contains inconsistent/incomplete content with the central pixel, resulting in many misclassifications especially in boundary and heterogeneous regions. So, a size-fixed square window is not enough for representing various terrain objects. To address this issue, we develop a content-adaptive multi-region deep network to obtain contextual consistent sampling windows for diverse terrain objects. Firstly, a complex scene of PolSAR image is partitioned into homogeneous, heterogeneous and boundary regions. Then, sampling windows with adaptive direction and scale are designed for three distinct regions. Besides, windows with central and global regions are proposed to provide additional local and global information. Finally, a fusion network is designed to adaptively combine different sampling windows to enhance classification performance. Experimental results on three real data sets demonstrate that the proposed method can achieve superior performance in both edge details and heterogeneous terrain objects compared with the state-of-the-art methods.

Deep learning models have demonstrated excellent performance for polarimetric SAR image classification. However, existing approaches generally use a fixed square window to sample image blocks as the network input, which may not effectively extract various terrain objects. To alleviate this issue, we proposed an adaptive region sampling network to learn different terrain types by introducing a novel sampling scheme with varying direction and scale. Initially, a complex PolSAR image is segmented into homogeneous, heterogeneous and boundary regions. Subsequently, small-scale and large-scale sampling windows are designed for homogeneous and heterogeneous regions, to capture local and global features for two types of regions respectively. Additionally, an adaptive directional sampling window is designed for boundary regions to ensure context consistency in the image block and prevent edge confusion. Experiments conducted on real PolSAR data sets demonstrate that our method achieves superior classification results, providing both regional consistency and boundary preservation.

The Convolutional Neural Network (CNN) model excels at learning local features, but struggles with capturing global large-scale features, particularly in extremely heterogeneous areas. In contrast, the Graph Convolution Network (GCN) is an effective tool for PolSAR image classification, demonstrating a capability to learn large-scale global features proficiently.To learn effective features for both heterogenous terrain objects and edge details well, a novel region partition based hybrid deep network is proposed for adaptive learning features for boundary and non-boundary regions, which can learn both large-scale global features for extremely heterogeneous terrain objects and pixel-wise features for edge details. The proposed method can effectively partition a PolSAR image into boundary and non-boundary regions, and design a CNN and GCN subnetworks for them respectively. Subsequently, a unified network is designed to effectively fuse both the advantages of GCN and CNN to enhance classification performance. The experiments verify the proposed algorithm can achieve better performance than compared methods in both region homogeneity and boundary preservation.

Recently, deep learning methods have achieved superior performance for polarimetric synthetic aperture radar (PolSAR) image classification. Existing deep learning methods learn PolSAR data by converting the covariance matrix into a feature vector or complex-valued vector as the input, learning features in Euclidean space. However, it is well-known that covariance matrices are manifold data endowing in Riemannian space instead of Euclidean space. Existing methods cannot learn the geometric characteristics of covariance matrices directly and destroy the channel correlation. To learn features from covariance matrices directly, we propose a lightweight Riemannian covariance matrix convolutional network (LRCM_CNN) for PolSAR classification for the first time, which directly utilizes the covariance matrix as the network input and defines the Riemannian operations to learn complex matrix’s features in Riemannian space. The proposed LRCM_CNN network initially designs a lightweight Riemannian covariance matrix network (LRCMnet) to learn covariance matrix features by exploiting a series of Riemannian convolution, rectified linear unit (ReLu), and LogEig operations in Riemannian space, which breaks through the Euclidean constraint of conventional networks. Then, features learned from covariance matrices are converted from Riemannian to Euclidean space, and a CNN module is appended to enhance contextual covariance matrix features. Besides, a fast kernel learning method is developed for the proposed method to learn class-specific features and reduce the computation time effectively, which implements the lightweight RCMnet. Experiments are conducted on four sets of real PolSAR data with different bands and sensors. Experiments results demonstrate the proposed method can obtain superior performance than the state-of-the-art methods.

In this paper, we propose a novel RGBD-based object 6DoF pose estimation network - RFFCE. It is a two-stage method that firstly leverages deep neural networks for feature extraction and object points matching, and then the geometric principles are utilized for final pose computation. Our approach consists of three primary innovations: residual feature fusion for representative RGBD feature extraction; confidence evaluation and confidence-based paired points offsets regression for self-evaluation and self-optimization respectively. Their effectiveness is verified through an ablation study, and our RFFCE achieves the SOTA performance on LineMOD, Occlusion-LineMOD and YCB-Video datasets. Additionally, we also conduct a real-world object grasping experiment for visualization and qualitative evaluation of the RFFCE.

Superpixel-based graph convolution network (SGCN) can extract global features well and reduce computing time greatly, which has been widely used in image classification. However, SGCN ignores individual feature for each pixel within a superpixel. Pixel-wise convolutional neural network (CNN) can learn local features with fixed-square convolution kernel. Combine with both the advantages of SGCN and CNN, we proposed a novel SGCN-CNN method, which can combine the global and local features together. Superpixel-wise SGC-N and pixel-wise CNN cannot be combined into a network directly since they are with different scales. To alleviate this issue, encoder and decoder are designed by defining an association matrix, which can covert features between superpixel and pixel. In addition, complex matrix-based Wishart metric is used to construct the edge weights for SGCN. The proposed method can obtain both global and local features to improve classification performance. Experimental results demonstrate the effectiveness of the proposed method.

Superpixel-based graph convolutional network (SGCN) has shown the advantages of less computational time and global modeling ability for polarimetric synthetic aperture radar (PolSAR) image classification. However, the effectiveness is heavily dependent on the superpixel segmentation result. Existing superpixel segmentation methods usually produce edge errors due to speckle and scattering confusion, which directly results in the mistakes of the final classification. To address this issue, a novel hybrid weighted fuzzy SGCN method(HF-SGCN) is proposed to correct the edge pixels by defining a fuzzy projection matrix (FPM). The FPM can transform features from superpixel to pixel, by which features of edge pixels can be calculated from all the neighboring superpixels with a certain probability, so as to correct edges to the most similar region. In addition, a hybrid weighted adjacent matrix is formulated by incorporating both the revised Wishart and multi-feature distances, which can enhance the discriminating features effectively. The proposed HF-SGCN method is capable of capturing the global contextual information and rectifying edges, while disregarding the local individual features for each pixel. To combine global and local features, we further propose the HF-SGCN-CNN method, which integrates the superpixel-wise HF-SGCN network and the pixel-wise 3D-CNN network into a unified framework. Thus, we can fuse the features extracted from two subnetworks, producing complementary global and local features that significantly improve classification accuracy. Experiments are conducted on four publicly real PolSAR datasets with different sensors and bands. Experimental results demonstrate the effectiveness of the proposed methods.

A novel m th best average biased received power (ABRP) user association (UA) scheme is proposed for K ‐tier heterogeneous networks, where a user is associated with the closest base station (BS) of the k th tier having the m th strongest ABRP, . By using stochastic geometry and Poisson point processes, the authors perform the analysis of the UA probability that a typical user is associated with a BS that has the m th strongest ABRP as well as the downlink signal‐to‐interference‐noise ratio (SINR) coverage performance. The obtained results show that, for a given tier k , when the layer's position in the ordered set of ABRPs is not equal to m and the power or density of BSs is relatively small, i.e., , the UA probability increases monotonically with or . Contrarily, it decreases. For the other UA probabilities, the opposite results are achieved. Additionally, the results also show that the coverage probability of the downlink SINR of the tier k always increases with power , which is consistent with the one in the conventionally best ABRP scheme. However, for other tiers, the corresponding downlink SINR coverage probabilities do not behave monotonically with the power .