A new approach to SAR image speckle filtering

2002 ◽  
Author(s):  
Wu Yiyong ◽  
Niu Ruixin ◽  
Peng Hailiang
Keyword(s):  
Sar Image ◽  
New Approach ◽  
Speckle Filtering

Speckle Filtering of Polarimetric SAR Image

2021 ◽  
pp. 323-346
Author(s):  
Ruliang Yang ◽  
Bowei Dai ◽  
Lulu Tan ◽  
Xiuqing Liu ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Sar Image ◽  
Polarimetric Sar ◽  
Speckle Filtering

scholarly journals A New Approach to the Histogram-Based Segmentation of Synthetic Aperture Radar Images

2021 ◽  
pp. 39-42
Author(s):  
Barbara Siemiątkowska ◽  
Krzysztof Gromada
Keyword(s):  
Mobile Robotics ◽  
Light Condition ◽  
Research Field ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Space System ◽  
New Approach ◽  
Radar Images ◽  
Optical Images ◽  
Images Processing

Radar machine vision is an emerging research field in the mobile robotics. Because Synthetic ApertureRadars (SAR) are robust against weather and light condition, they provide more useful and reliable in formation than optical images. On the other hand, the data processing is more complicated and less researched than visible light images processing. The main goal of our reasarch is to build simple and efficient method of SAR image analysis. In this article we describe our research related to SAR image segmenta tion and attempts to detect elements such as the build ings, roads and forest areas. Tests were carried out for the images made available by Leonardo Airborne & Space System Company.

scholarly journals APPLICATION OF A NEW POLARIMETRIC FILTER TO RADARSAT-2 DATA OF DECEPTION ISLAND (ANTARCTIC PENINSULA REGION) FOR SURFACE COVER CHARACTERIZATION

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1035-1039
Author(s):  
S. Guillaso ◽  
T. Schmid ◽  
J. López-Martínez ◽  
O. D'Hondt
Keyword(s):  
Antarctic Peninsula ◽  
Land Surface ◽  
Supervised Classification ◽  
Image Data ◽  
Bilateral Filter ◽  
Support Vector ◽  
Svm Classifier ◽  
Sar Image ◽  
Deception Island ◽  
New Approach

In this paper, we describe a new approach to analyse and quantify land surface covers on Deception Island, a volcanic island located in the Northern Antarctic Peninsula region by means of fully polarimetric RADARSAT-2 (C-Band) SAR image. Data have been filtered by a new polarimetric speckle filter (PolSAR-BLF) that is based on the bilateral filter. This filter is locally adapted to the spatial structure of the image by relying on pixel similarities in both the spatial and the radiometric domains. Thereafter different polarimetric features have been extracted and selected before being geocoded. These polarimetric parameters serve as a basis for a supervised classification using the Support Vector Machine (SVM) classifier. Finally, a map of landform is generated based on the result of the SVM results.

scholarly journals SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
XiuXia Ji ◽  
Yinan Sun
Keyword(s):  
Sparse Representation ◽  
Target Recognition ◽  
Sar Image ◽  
Recognition Method ◽  
Average Correlation ◽  
Monogenic Signal ◽  
New Approach ◽  
Training Samples ◽  
Overcomplete Dictionary ◽  
Feature Information

It is necessary to recognize the target in the situation of military battlefield monitoring and civilian real-time monitoring. Sparse representation-based SAR image target recognition method uses training samples or feature information to construct an overcomplete dictionary, which will inevitably affect the recognition speed. In this paper, a method based on monogenic signal and sparse representation is presented for SAR image target recognition. In this method, the extended maximum average correlation height filter is used to train the samples and generate the templates. The monogenic features of the templates are extracted to construct subdictionaries, and the subdictionaries are combined to construct a cascade dictionary. Sparse representation coefficients of the testing samples over the cascade dictionary are calculated by the orthogonal matching tracking algorithm, and recognition is realized according to the energy of the sparse coefficients and voting recognition. The experimental results suggest that the new approach has good results in terms of recognition accuracy and recognition time.

scholarly journals Deep Learning for SAR Image Despeckling

2019 ◽  
Vol 11 (13) ◽  
pp. 1532 ◽  
Author(s):  
Francesco Lattari ◽  
Borja Gonzalez Leon ◽  
Francesco Asaro ◽  
Alessio Rucci ◽  
Claudio Prati ◽  
...  
Keyword(s):  
Learning Strategy ◽  
Real Data ◽  
Image Features ◽  
Fine Tuning ◽  
Sar Image ◽  
Two Phase ◽  
Promising Alternative ◽  
Image Domain ◽  
Texture Preservation ◽  
Speckle Filtering

Speckle filtering is an unavoidable step when dealing with applications that involve amplitude or intensity images acquired by coherent systems, such as Synthetic Aperture Radar (SAR). Speckle is a target-dependent phenomenon; thus, its estimation and reduction require the individuation of specific properties of the image features. Speckle filtering is one of the most prominent topics in the SAR image processing research community, who has first tackled this issue using handcrafted feature-based filters. Even if classical algorithms have slowly and progressively achieved better and better performance, the more recent Convolutional-Neural-Networks (CNNs) have proven to be a promising alternative, in the light of the outstanding capabilities in efficiently learning task-specific filters. Currently, only simplistic CNN architectures have been exploited for the speckle filtering task. While these architectures outperform classical algorithms, they still show some weakness in the texture preservation. In this work, a deep encoder–decoder CNN architecture, focused in the specific context of SAR images, is proposed in order to enhance speckle filtering capabilities alongside texture preservation. This objective has been addressed through the adaptation of the U-Net CNN, which has been modified and optimized accordingly. This architecture allows for the extraction of features at different scales, and it is capable of producing detailed reconstructions through its system of skip connections. In this work, a two-phase learning strategy is adopted, by first pre-training the model on a synthetic dataset and by adapting the learned network to the real SAR image domain through a fast fine-tuning procedure. During the fine-tuning phase, a modified version of the total variation (TV) regularization was introduced to improve the network performance when dealing with real SAR data. Finally, experiments were carried out on simulated and real data to compare the performance of the proposed method with respect to the state-of-the-art methodologies.

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