Identification of Statistically Homogeneous Pixels Based on One-Sample Test

Lin, Keng-Fan; Perissin, Daniele

doi:10.3390/rs9010037

Cited by 20 publications

(14 citation statements)

References 22 publications

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“…However, these tests cannot handle possible temporal variability. To reduce the impact of temporal variability during the SHP identification process, we apply the robust t-test (TR) developed in our recent studies (see [19] for details) to improve the effectiveness of the test operation. The application of the TR test helps to identify the SHP with assurances of similar temporal behaviors.…”

Section: Adaptive Extraction Of Spatio-temporal Observationsmentioning

confidence: 99%

Single-Polarized SAR Classification Based on a Multi-Temporal Image Stack

Lin

Perissin

2018

Remote Sensing

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Land cover classification plays a pivotal role in Earth resource management. In the past, synthetic aperture radar (SAR) had been extensively studied for classification. However, limited work has been done on multi-temporal datasets owing to the lack of data availability and computational power. As Earth observation (EO) becomes more and more imperative, it becomes essential to exploit the information embedded in multi-temporal datasets. In this paper, we present a framework for SAR pixel labeling. Specifically, we exploit spatio-temporal information for pixel labeling. The proposed scheme includes four steps: (1) extraction of spatio-temporal observations; (2) feature computation; (3) feature reduction and (4) pixel labeling. First, an adaptive approach is applied to the data cube to extract spatio-temporal observations in both coherent and incoherent domains. Second, features in distinct domains are designed and computed to boost information content embedded in the multi-temporal datasets. Third, sequential feature selection is utilized for selecting the most discriminative features among the entire feature space. Last, the discriminative classifier is used to label the class of each pixel. By integrating pixel-/object-based processing techniques, spatial/temporal observations and coherent/incoherent data attributes, the proposed method explores diverse observations to solve complex labeling problems. In the experiments, we apply the proposed method on 64 TanDEM-X images and 70 COSMO-SkyMed high-resolution images, respectively. Both experiments reveal high accuracies for multi-class labeling. The proposed technique, therefore, provides a new solution for classifying multi-temporal single-polarized datasets.

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Section: Adaptive Extraction Of Spatio-temporal Observationsmentioning

confidence: 99%

Single-Polarized SAR Classification Based on a Multi-Temporal Image Stack

Lin

Perissin

2018

Remote Sensing

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“…Different statistical methods have already been proposed to identify the SHP families. These include using two-sample Kolmogorov-Smirnov (KS) test [32], the mean amplitude and the mean amplitude difference [37], exploiting polarimetry information [38], confidence interval according to the central limit theorem [39], one-sample test [40], similarity test of the covariance matrix [41], geometric distance and target features [42], various amplitude statistics [43], and non-local filters [44,45]. In this study, we followed the approach proposed by [32] in SqueeSAR technique and implemented a new method using the two-sample t-test for DS selection.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we followed the approach proposed by [32] in SqueeSAR technique and implemented a new method using the two-sample t-test for DS selection. Compared to the one-sample test implemented in [40], which assumes that the samples are dependent, the two-sample t-test does not consider dependency assumption between the samples. We analyzed the efficiency of the t-test in terms of processing speed, sensitivity to the number of images and reliability of the results using 50 Sentinel-1 SAR images acquired over Trondheim in Norway.…”

Section: Introductionmentioning

confidence: 99%

Efficient Ground Surface Displacement Monitoring Using Sentinel-1 Data: Integrating Distributed Scatterers (DS) Identified Using Two-Sample t-Test with Persistent Scatterers (PS)

et al. 2018

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Combining persistent scatterers (PS) and distributed scatterers (DS) is important for effective displacement monitoring using time-series of SAR data. However, for large stacks of synthetic aperture radar (SAR) data, the DS analysis using existing algorithms becomes a time-consuming process. Moreover, the whole procedure of DS selection should be repeated as soon as a new SAR acquisition is made, which is challenging considering the short repeat-observation of missions such as Sentinel-1. SqueeSAR is an approach for extracting signals from DS, which first applies a spatiotemporal filter on images and optimizes DS, then incorporates information from both optimized DS and PS points into interferometric SAR (InSAR) time-series analysis. In this study, we followed SqueeSAR and implemented a new approach for DS analysis using two-sample t-test to efficiently identify neighboring pixels with similar behaviour. We evaluated the performance of our approach on 50 Sentinel-1 images acquired over Trondheim in Norway between January 2015 and December 2016. A cross check on the number of the identified neighboring pixels using the Kolmogorov-Smirnov (KS) test, which is employed in the SqueeSAR approach, and the t-test shows that their results are strongly correlated. However, in comparison to KS-test, the t-test is less computationally intensive (98% faster). Moreover, the results obtained by applying the tests under different SAR stack sizes from 40 to 10 show that the t-test is less sensitive to the number of images.

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“…Reference [9] used the Kolmogorov-Smirnov test to evaluate the similarity of the amplitude distribution in two image pixels, and [12] applied the Anderson-Darling test statistic. Reference [22] applied a robust t-test to improve the effectiveness of identifying statistical homogenous pixels (SHPs). With the availability of fully polarimetric data, [25] applied a likelihood ratio test to establish the similarity of two Wishart matrices in two pixels.…”

Section: Introductionmentioning

confidence: 99%

Polarimetry-Based Distributed Scatterer Processing Method for PSI Applications

Mullissa

Perissin

Tolpekin

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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Permanent scatterer interferometry is a multitemporal interferometric synthetic aperture radar technique that produces high-accuracy ground deformation measurement. A high density of permanent scatterer (PS) is required to provide accurate results. In natural environments with low PS density, distributed scatterers (DSs) could serve as additional coherent observations. This paper introduces a polarimetric scattering property-based adaptive filtering method that preserves PS candidates and filters DS candidates. To further increase the coherence estimate of DS candidates, the technique includes a complex coherence decomposition that adaptively selects the most stable scattering mechanisms, thus improving pixel coherence estimation. The proposed method was evaluated on 11 quad-polarized ALOS PALSAR images and 21 dual-polarized Sentinel-1 images acquired over San Fernando Valley, CA, USA, and Groningen, The Netherlands, respectively. The application of this method increased the number of coherent pixels by almost a factor of eight compared with a single-polarization channel. This paper concludes that a coherence estimate can be significantly improved by applying scattering property-based adaptive filtering and coherence matrix decomposition and accurate displacement measurements can be achieved.

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Identification of Statistically Homogeneous Pixels Based on One-Sample Test

Cited by 20 publications

References 22 publications

Single-Polarized SAR Classification Based on a Multi-Temporal Image Stack

Single-Polarized SAR Classification Based on a Multi-Temporal Image Stack

Efficient Ground Surface Displacement Monitoring Using Sentinel-1 Data: Integrating Distributed Scatterers (DS) Identified Using Two-Sample t-Test with Persistent Scatterers (PS)

Polarimetry-Based Distributed Scatterer Processing Method for PSI Applications

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