scholarly journals FULL POLARIMETRIC UAVSAR IMAGE CHANGE DETECTION BASED ON CHANGE INDICES

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1107-1111
Author(s):  
L. Yousefizadeh ◽  
R. Shahhoseini ◽  
S. Homayouni
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
San Andreas ◽  
L Band ◽  
Image Change Detection ◽  
Dimension Space ◽  
Change Detection Performance

Abstract. Change detection is one of the most important applications of Polarimetric Synthetic Aperture Radar (PolSAR) data in monitoring urban development and supporting urban planning due to the sensibility of SAR signal to geometrical and physical properties of terrestrial features. In this paper, we proposed an unsupervised change detection method using change indices extracted from PolSAR images. Kernel k-means clustering was then performed to extract changed areas. The kernel k-means clustering is an unsupervised algorithm that maps the input features to higher Hilbert dimension space by using a kernel function. To better representation of changed areas, different change indices were generated. The method was applied to UAVSAR L-band SAR images acquired over an urban area in San Andreas, United States. We evaluated the change detection performance based on kappa and overall accuracies of the proposed approach and compared with other well-known classic methods.

scholarly journals Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

2021 ◽  
Vol 13 (7) ◽  
pp. 1236
Author(s):  
Yuanjun Shu ◽  
Wei Li ◽  
Menglong Yang ◽  
Peng Cheng ◽  
Songchen Han
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Change Detection ◽  
Detection Method ◽  
Training Data ◽  
Synthetic Aperture ◽  
Learning Method ◽  
Sar Images ◽  
Two Stage ◽  
Second Stage

Convolutional neural networks (CNNs) have been widely used in change detection of synthetic aperture radar (SAR) images and have been proven to have better precision than traditional methods. A two-stage patch-based deep learning method with a label updating strategy is proposed in this paper. The initial label and mask are generated at the pre-classification stage. Then a two-stage updating strategy is applied to gradually recover changed areas. At the first stage, diversity of training data is gradually restored. The output of the designed CNN network is further processed to generate a new label and a new mask for the following learning iteration. As the diversity of data is ensured after the first stage, pixels within uncertain areas can be easily classified at the second stage. Experiment results on several representative datasets show the effectiveness of our proposed method compared with several existing competitive methods.

scholarly journals Novel Techniques for Built-Up Area Extraction from Polarimetric SAR Images

2020 ◽  
Author(s):  
D Ratha ◽  
P Gamba ◽  
A Bhattacharya ◽  
Alejandro Frery
Keyword(s):  
Remote Sensing ◽  
Unit Sphere ◽  
Geodesic Distance ◽  
Synthetic Aperture ◽  
Extraction Techniques ◽  
Remote Sensing Images ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Urban Scenes ◽  
L Band

© 2004-2012 IEEE. Built-up (BU) area extraction from remote sensing images is important to monitor and manage urbanization and industrialization. In this letter, we propose two BU area extraction techniques based on the analysis of fully polarimetric synthetic aperture radar (PolSAR) data. Both methods exploit the geodesic distance on the unit sphere in the space of Kennaugh matrices. The first method is based on the three dominant scattering types in the scene and compares them with scattering models; if any of them matches with BU type elementary scattering models, then the pixel is said to belong to a BU area. The second method is based on a novel PolSAR BU index (RBUI) composed by considering scattering mechanisms from BU structures. The two proposed techniques are validated on two different urban scenes, one acquired at C-band by RADARSAT-2 and other at L-band by ALOS-2 SAR sensors.

scholarly journals Novel Techniques for Built-Up Area Extraction from Polarimetric SAR Images

2020 ◽  
Author(s):  
D Ratha ◽  
P Gamba ◽  
A Bhattacharya ◽  
Alejandro Frery
Keyword(s):  
Remote Sensing ◽  
Unit Sphere ◽  
Geodesic Distance ◽  
Synthetic Aperture ◽  
Extraction Techniques ◽  
Remote Sensing Images ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Urban Scenes ◽  
L Band

© 2004-2012 IEEE. Built-up (BU) area extraction from remote sensing images is important to monitor and manage urbanization and industrialization. In this letter, we propose two BU area extraction techniques based on the analysis of fully polarimetric synthetic aperture radar (PolSAR) data. Both methods exploit the geodesic distance on the unit sphere in the space of Kennaugh matrices. The first method is based on the three dominant scattering types in the scene and compares them with scattering models; if any of them matches with BU type elementary scattering models, then the pixel is said to belong to a BU area. The second method is based on a novel PolSAR BU index (RBUI) composed by considering scattering mechanisms from BU structures. The two proposed techniques are validated on two different urban scenes, one acquired at C-band by RADARSAT-2 and other at L-band by ALOS-2 SAR sensors.

A Novel SAR Images Change Detection Method Based on Dynamic TUNET-CRF Model

2021 ◽  
Author(s):  
Jianlong Zhang ◽  
Mengying Cui ◽  
Bin Wang ◽  
Chen Chen ◽  
Yang Zhou ◽  
...  
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Sar Images

A variational change detection method for multitemporal SAR images

2014 ◽  
Vol 5 (4) ◽  
pp. 342-351 ◽  
Author(s):  
Yin Chen ◽  
Armin B. Cremers ◽  
Zhiguo Cao
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Sar Images

scholarly journals Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

2020 ◽  
Vol 12 (3) ◽  
pp. 548 ◽  
Author(s):  
Xinzheng Zhang ◽  
Guo Liu ◽  
Ce Zhang ◽  
Peter M. Atkinson ◽  
Xiaoheng Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Speckle Noise ◽  
Sar Image ◽  
Phase Object ◽  
Sar Images ◽  
Two Phase ◽  
Object Based ◽  
Multi Temporal ◽  
Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.

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