Multiple Mode SAR Raw Data Simulation and Parallel Acceleration for Gaofen-3 Mission

Zhang, Fan; Yao, Xiaojie; Tang, Hanyuan; Yin, Qiang; Hu, Yuxin; Lei, Bin

doi:10.1109/jstars.2017.2787728

Cited by 69 publications

(41 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the Section 1, we know that PS 3 VM-D is also a semi-supervised method, which considers reliable incremental samples as semi-supervised samples by sample similarity. Therefore, PS 3 configuration is shown in Table 1. With the change of the number of the labeled samples, the OA trend chart of two methods is obtained, as shown in Figure 13.…”

Section: Verifying the Recognition Performance Ofmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Semi‐Supervised Learning Method Based on Fast Search and Density Peaks

et al. 2019

View full text Add to dashboard Cite

Radar image recognition is a hotspot in the field of remote sensing. Under the condition of sufficiently labeled samples, recognition algorithms can achieve good classification results. However, labeled samples are scarce and costly to obtain. Our major interest in this paper is how to use these unlabeled samples to improve the performance of a recognition algorithm in the case of limited labeled samples. This is a semi-supervised learning problem. However, unlike the existing semi-supervised learning methods, we do not use unlabeled samples directly and, instead, look for safe and reliable unlabeled samples before using them. In this paper, two new semi-supervised learning methods are proposed: a semi-supervised learning method based on fast search and density peaks (S2DP) and an iterative S2DP method (IS2DP). When the labeled samples satisfy a certain requirement, S2DP uses fast search and a density peak clustering method to detect reliable unlabeled samples based on the weighted kernel Fisher discriminant analysis (WKFDA). Then, a labeling method based on clustering information (LCI) is designed to label the unlabeled samples. When the labeled samples are insufficient, IS2DP is used to iteratively search for reliable unlabeled samples for semi-supervision. Then, these samples are added to the labeled samples to improve the recognition performance of S2DP. In the experiments, real radar images are used to verify the performance of our proposed algorithm in dealing with the scarcity of the labeled samples. In addition, our algorithm is compared against several semi-supervised deep learning methods with similar structures. Experimental results demonstrate that the proposed algorithm has better stability than these methods.

show abstract

Section: Verifying the Recognition Performance Ofmentioning

confidence: 99%

“…Radar image recognition is a popular research area in the field of remote sensing [1][2][3]. With the development of imaging technologies and the expansion of radar image data, the requirement of real-time and accuracy of data processing becomes higher and higher.…”

Section: Introductionmentioning

confidence: 99%

A Novel Semi‐Supervised Learning Method Based on Fast Search and Density Peaks

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In the heterogeneous soil model, OpenMP parallel optimization is used for multi-core parallelism implementation [27]. In our previous work, various parallel mechanisms have been introduced to accelerate the SAR raw data simulation, including clouding computing, GPU parallel, CPU parallel, and hybrid CPU/GPU parallel [28][29][30][31][32][33][34][35]. As far as the inversion algorithms are concerned, the time cost is only minute-level.…”

Section: Introductionmentioning

confidence: 99%

GPU-Based Soil Parameter Parallel Inversion for PolSAR Data

Yin¹,

You²,

Zhang³

et al. 2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

With the development of polarimetric synthetic aperture radar (PolSAR), quantitative parameter inversion has been seen great progress, especially in the field of soil parameter inversion, which has achieved good results for applications. However, PolSAR data is also often many terabytes large. This huge amount of data also directly affects the efficiency of the inversion. Therefore, the efficiency of soil moisture and roughness inversion has become a problem in the application of this PolSAR technique. A parallel realization based on a graphics processing unit (GPU) for multiple inversion models of PolSAR data is proposed in this paper. This method utilizes the high-performance parallel computing capability of a GPU to optimize the realization of the surface inversion models for polarimetric SAR data. Three classical forward scattering models and their corresponding inversion algorithms are analyzed. They are different in terms of polarimetric data requirements, application situation, as well as inversion performance. Specifically, the inversion process of PolSAR data is mainly improved by the use of the high concurrent threads of GPU. According to the inversion process, various optimization strategies are applied, such as the parallel task allocation, and optimizations of instruction level, data storage, data transmission between CPU and GPU. The advantages of a GPU in processing computationally-intensive data are shown in the data experiments, where the efficiency of soil roughness and moisture inversion is increased by one or two orders of magnitude.

show abstract

“…Graph belongs to non-Euclidean structures, and the number of the adjacent nodes of different nodes is not the same. Non-spatial methods aim at transforming graph-structure data into the Euclidean structure by redefining the neighbour regions of nodes [27], so that all the nodes have the same number of the adjacent nodes and the traditional CNN [28] can process the data. These types of approaches generally have two steps: (1) select the most representative nodes to form the sequence of the nodes to be segmented, and (2) define a fixed size neighbouring field for each selected node.…”

Section: Introductionmentioning

confidence: 99%

Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data

Gao

Sun

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learning attempt to take superpixels as processing units. However, the over-segmented images become non-Euclidean structure data that traditional deep Convolutional Neural Networks (CNN) cannot directly process. Here, we propose a novel Attention Graph Convolution Network (AGCN) to perform superpixel-wise segmentation in big SAR imagery data. AGCN consists of an attention mechanism layer and Graph Convolution Networks (GCN). GCN can operate on graph-structure data by generalizing convolutions to the graph domain and have been successfully applied in tasks such as node classification. The attention mechanism layer is introduced to guide the graph convolution layers to focus on the most relevant nodes in order to make decisions by specifying different coefficients to different nodes in a neighbourhood. The attention layer is located before the convolution layers, and noisy information from the neighbouring nodes has less negative influence on the attention coefficients. Quantified experiments on two airborne SAR image datasets prove that the proposed method outperforms the other state-of-the-art segmentation approaches. Its computation time is also far less than the current mainstream pixel-level semantic segmentation networks.

show abstract

Multiple Mode SAR Raw Data Simulation and Parallel Acceleration for Gaofen-3 Mission

Cited by 69 publications

References 38 publications

A Novel Semi‐Supervised Learning Method Based on Fast Search and Density Peaks

A Novel Semi‐Supervised Learning Method Based on Fast Search and Density Peaks

GPU-Based Soil Parameter Parallel Inversion for PolSAR Data

Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data

Contact Info

Product

Resources

About