scholarly journals Virtual Antenna Array Analysis for MIMO Synthetic Aperture Radars

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Wen-Qin Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Antenna Array ◽  
Antenna Arrays ◽  
Multiple Antennas ◽  
Synthetic Aperture ◽  
Conceptual System ◽  
Signal Processing Algorithm ◽  
Sensing Applications ◽  
Wide Swath

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) that employs multiple antennas to transmit orthogonal waveforms and multiple antennas to receive radar echoes is a recently proposed remote sensing concept. It has been shown that MIMO SAR can be used to improve remote sensing system performance. Most of the MIMO SAR research so far focused on signal/data models and corresponding signal processing algorithm. Little work related to MIMO SAR antenna analysis can be found. One of the main advantages of MIMO SAR is that the degrees of freedom can be greatly increased by the concept of virtual antenna array. In this paper, we analyze the virtual antenna array for MIMO SAR high-resolution wide-swath remote sensing applications. The one-dimensional uniform and nonuniform linear antenna arrays are investigated and their application potentials in high-resolution wide-swath remote sensing are introduced. The impacts of nonuniform spatial sampling in the virtual antenna array are analyzed, along with a multichannel filtering-based reconstruction algorithm. Conceptual system and discussions are provided. It is shown that high operation flexibility and reconfigurability can be obtained by utilizing the virtual antenna arrays provided by the MIMO SAR systems, thus enabling a satisfactory remote sensing performance.

scholarly journals A Novel Orthogonal Waveform Separation Scheme for Airborne MIMO-SAR Systems

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3580 ◽  
Author(s):  
Jie Wang ◽  
Ke-Hong Zhu ◽  
Li-Na Wang ◽  
Xing-Dong Liang ◽  
Long-Yong Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Synthetic Aperture Radar ◽  
3D Imaging ◽  
Synthetic Aperture ◽  
Separation Scheme ◽  
Sar Images ◽  
Wide Swath ◽  
Aperture Radar

In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems is seriously limited. This is mainly because the superposed echoes of the multiple transmitted orthogonal waveforms cannot be separated perfectly. The imperfect separation will introduce ambiguous energy and degrade SAR images dramatically. In this paper, a novel orthogonal waveform separation scheme based on echo-compression is proposed for airborne MIMO-SAR systems. Specifically, apart from the simultaneous transmissions, the transmitters are required to radiate several times alone in a synthetic aperture to sense their private inner-aperture channels. Since the channel responses at the neighboring azimuth positions are relevant, the energy of the solely radiated orthogonal waveforms in the superposed echoes will be concentrated. To this end, the echoes of the multiple transmitted orthogonal waveforms can be separated by cancelling the peaks. In addition, the cleaned echoes, along with original superposed one, can be used to reconstruct the unambiguous echoes. The proposed scheme is validated by simulations.

scholarly journals High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

2015 ◽  
Vol XL-7/W4 ◽  
pp. 197-201 ◽  
Author(s):  
Y. Yang ◽  
H. T. Li ◽  
Y. S. Han ◽  
H. Y. Gu
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Graph Theory ◽  
High Resolution ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Sensing Applications ◽  
Initial Object ◽  
Object Layer ◽  
Evolution Approach

Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

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