scholarly journals Spatially Diverse ISAR Imaging for Classification Performance Enhancement

2011 ◽  
Vol 57 (1) ◽  
pp. 15-21 ◽  
Author(s):  
Stefan Brisken ◽  
Dietmar Matthes ◽  
Torsten Mathy ◽  
Josef Worms
Keyword(s):  
Performance Enhancement ◽  
Target Identification ◽  
Classification Performance ◽  
Sar Images ◽  
Synchronization Errors ◽  
Aspect Angle ◽  
Scattering Centers ◽  
Isar Imaging ◽  
Spatially Distributed ◽  
Inverse Sar

Spatially Diverse ISAR Imaging for Classification Performance Enhancement One popular approach to the problem of Non-Cooperative Target Identification is the use of 2D Inverse SAR images. Methods to successfully identify a target include the comparison of a set of scattering centers in the ISAR image to a database or the estimation of target dimensions. While working well in theory, these techniques face major difficulties in practice. In the conventional case of a monostatic radar, visibility of scattering centers varies with the target aspect angle due to fading. In this paper we examine that the visibility of scattering centers can be improved by incoherent addition of images from spatially distributed radars. The main focus lies in the description and results of a multistatic ISAR experiment carried out at Fraunhofer FHR. We display theoretically derived bistatic synchronization errors in a practical system and formulate additional multistatic synchronization requirements, necessary to add up the images.

scholarly journals Superresolution Polarimetric ISAR Imaging Based on 2D CP-GTD Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Da-hai Dai ◽  
Jing-ke Zhang ◽  
Xue-song Wang ◽  
Shun-ping Xiao
Keyword(s):  
Real Data ◽  
Scattering Matrix ◽  
Geometrical Theory ◽  
Scattering Mechanism ◽  
New Approach ◽  
Scattering Centers ◽  
Isar Imaging ◽  
Geometrical Theory Of Diffraction ◽  
The One ◽  
Better Than

This paper presented a new approach to superresolution ISAR imaging based on a scattering model called coherent polarized geometrical theory of diffraction (CP-GTD) which is better matched to the physical scattering mechanism. The algorithm is a joint processing between polarization and superresolution essentially. It can also estimate the number, position, frequency dependence, span, and normalized scattering matrix of scattering centers instantaneously for each channel rather than the one which extracts parameters from each channel separately, and its performance is better than the latter because the fully polarized information is used. The superiority of the CP-GTD is verified by experiment results based on simulated and real data.

scholarly journals Classification of Multi-Frequency Polarimetric SAR Images Based on Multi-Linear Subspace Learning of Tensor Objects

2015 ◽  
Vol 7 (7) ◽  
pp. 9253-9268 ◽  
Author(s):  
Chun Liu ◽  
Junjun Yin ◽  
Jian Yang ◽  
Wei Gao
Keyword(s):  
Linear Subspace ◽  
Principal Component ◽  
Subspace Learning ◽  
Classification Performance ◽  
Polarimetric Sar ◽  
Third Order ◽  
Sar Images ◽  
Linear Discriminant ◽  
Multi Band

One key problem for the classification of multi-frequency polarimetric SAR images is to extract target features simultaneously in the aspects of frequency, polarization and spatial texture. This paper proposes a new classification method for multi-frequency polarimetric SAR data based on tensor representation and multi-linear subspace learning (MLS). Firstly, each cell of the SAR images is represented by a third-order tensor in the frequency, polarization and spatial domains, with each order of tensor corresponding to one domain. Then, two main MLS methods, i.e., multi-linear principal component analysis (MPCA) and multi-linear extension of linear discriminant analysis (MLDA), are used to learn the third-order tensors. MPCA is used to analyze the principal component of the tensors. MLDA is applied to improve the discrimination between different land covers. Finally, the lower dimension subtensor features extracted by the MPCA and MLDA algorithms are classified with a neural network (NN) classifier. The classification scheme is accessed using multi-band polarimetric SAR images (C-, L- and P-band) acquired by the Airborne Synthetic Aperture Radar (AIRSAR) sensor of the Jet Propulsion Laboratory (JPL) over the Flevoland area. Experimental results demonstrate that the proposed method has good classification performance in comparison with the classic multi-band Wishart classifier. The overall classification accuracy is close to 99%, even when the number of training samples is small.

scholarly journals Estimation of Soil Moisture in an Alpine Catchment with RADARSAT2 Images

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
L. Pasolli ◽  
C. Notarnicola ◽  
L. Bruzzone ◽  
G. Bertoldi ◽  
S. Della Chiesa ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Support Vector ◽  
Distributed Data ◽  
Remotely Sensed Data ◽  
Ancillary Data ◽  
Sar Images ◽  
Biophysical Parameter ◽  
Spatially Distributed ◽  
Microwave Signals ◽  
The Alps

Soil moisture retrieval is one of the most challenging problems in the context of biophysical parameter estimation from remotely sensed data. Typically, microwave signals are used thanks to their sensitivity to variations in the water content of soil. However, especially in the Alps, the presence of vegetation and the heterogeneity of topography may significantly affect the microwave signal, thus increasing the complexity of the retrieval. In this paper, the effectiveness of RADARSAT2 SAR images for the estimation of soil moisture in an alpine catchment is investigated. We first carry out a sensitivity analysis of the SAR signal to the moisture content of soil and other target properties (e.g., topography and vegetation). Then we propose a technique for estimating soil moisture based on the Support Vector Regression algorithm and the integration of ancillary data. Preliminary results are discussed both in terms of accuracy over point measurements and effectiveness in handling spatially distributed data.

scholarly journals Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

2020 ◽  
Vol 14 (3) ◽  
pp. 935-956 ◽  
Author(s):  
Carlo Marin ◽  
Giacomo Bertoldi ◽  
Valentina Premier ◽  
Mattia Callegari ◽  
Christian Brida ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Melting Process ◽  
Liquid Water Content ◽  
Sar Images ◽  
Distributed Information ◽  
Snow Modeling ◽  
Alpine Regions ◽  
Snow Properties ◽  
Wet Snow ◽  
Spatially Distributed

Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

scholarly journals Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3576 ◽  
Author(s):  
Peng Zhang ◽  
Lifu Chen ◽  
Zhenhong Li ◽  
Jin Xing ◽  
Xuemin Xing ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Classification Performance ◽  
Sar Images ◽  
Convolutional Network ◽  
Rich Information ◽  
Multi Level ◽  
The Difference ◽  
Score Map ◽  
Occurrence Matrix ◽  
New Framework

The water and shadow areas in SAR images contain rich information for various applications, which cannot be extracted automatically and precisely at present. To handle this problem, a new framework called Multi-Resolution Dense Encoder and Decoder (MRDED) network is proposed, which integrates Convolutional Neural Network (CNN), Residual Network (ResNet), Dense Convolutional Network (DenseNet), Global Convolutional Network (GCN), and Convolutional Long Short-Term Memory (ConvLSTM). MRDED contains three parts: the Gray Level Gradient Co-occurrence Matrix (GLGCM), the Encoder network, and the Decoder network. GLGCM is used to extract low-level features, which are further processed by the Encoder. The Encoder network employs ResNet to extract features at different resolutions. There are two components of the Decoder network, namely, the Multi-level Features Extraction and Fusion (MFEF) and Score maps Fusion (SF). We implement two versions of MFEF, named MFEF1 and MFEF2, which generate separate score maps. The difference between them lies in that the Chained Residual Pooling (CRP) module is utilized in MFEF2, while ConvLSTM is adopted in MFEF1 to form the Improved Chained Residual Pooling (ICRP) module as the replacement. The two separate score maps generated by MFEF1 and MFEF2 are fused with different weights to produce the fused score map, which is further handled by the Softmax function to generate the final extraction results for water and shadow areas. To evaluate the proposed framework, MRDED is trained and tested with large SAR images. To further assess the classification performance, a total of eight different classification frameworks are compared with our proposed framework. MRDED outperformed by reaching 80.12% in Pixel Accuracy (PA) and 73.88% in Intersection of Union (IoU) for water, 88% in PA and 77.11% in IoU for shadow, and 95.16% in PA and 90.49% in IoU for background classification, respectively.

Three-dimensional positions of scattering centers reconstruction from multiple SAR images based on radargrammetry

2015 ◽  
Vol 22 (5) ◽  
pp. 1776-1789 ◽  
Author(s):  
Jin-rong Zhong ◽  
Gong-jian Wen ◽  
Bing-wei Hui ◽  
De-ren Li
Keyword(s):  
Three Dimensional ◽  
Sar Images ◽  
Scattering Centers
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