In this paper, we will present a determinant ratio test (DRT) statistic to measure the similarity of two covariance matrices for unsupervised change detection in polarimetric radar images. The multilook complex covariance matrix is assumed to follow a scaled complex Wishart distribution. In doing so, the distribution of the DRT statistic is analytically derived which is exactly Wilks’s lambda of the second kind distribution, with density expressed in terms of Meijer G-functions. Due to this distribution, the constant false alarm rate (CFAR) algorithm is derived in order to achieve the required performance. More specifically, a threshold is provided by the CFAR to apply to the DRT statistic producing a binary change map. Finally, simulated and real multilook polarimetric radar data are employed to assess the performance of the method and is compared with the Hotelling–Lawley trace (HLT) statistic.

Water hyacinth (Pontederia crassipes) is recognised as the most notorious invasive species worldwide. Although its threats and effects are fully documented, its spatial distribution is still poorly understood, especially in complex environments such as wetland systems. This study aimed mapping the spatiotemporal distribution of invasive water hyacinth (WH) in Anzali International Wetland (AIW), whose habitat is endangered by the presence of WH; it was conducted using Sentinel-2 Multi-Spectral Instrument (MSI) 2022 data. Specifically, this study sought to identify multispectral remote sensing variables and in-situ field data using machine learning (ML) methods to detect and map WH growth stages. We used four images dominated by four growth stages: early, mid, high, and decaying stages to train our ML classifier. We used Random Forest (RF) algorithm for training our training samples achieving an overall classification accuracy (OA) of over 98%. These findings were further supported by statistical analysis, such as F1 (above 96%) and Intersection over Union (IoU) (above 92%), indicating the high performance quality of the used algorithm. Our study provides valuable insights into using ML algorithms for mapping WH growth stages, which can significantly contribute to can help decision-makers to take necessary measures to manage the spread of water hyacinth with multiple growth stages in the same region.

According to the literature, the utilization of spatial features can significantly enhance the accuracy of hyperspectral image (HSI) classification. Fractal dimension (FD) features are powerful measures of texture, representing the local complexity of an image. In HSI classification, textural features are typically extracted from dimensionally reduced datacubes, such as principal component analysis (PCA). However, the effectiveness of textures obtained from alternative feature extraction methods in improving classification accuracy has not been extensively investigated. This study introduces a new ensemble support vector machine classification system that combines spectral features derived from PCA, minimum noise fraction, linear discriminant analysis, and FD features derived from these feature extraction methods. The final results on two HSI datasets, namely Indian Pines and Pavia University, demonstrate that the proposed classification method achieves approximately 95.75% and 99.36% accuracies, outperforming several other spatial-spectral HSI classification methods.

Forest clear-cut detection would be valuable for forest management, if it could be done routinely in near-real-time with a spaceborne synthetic aperture radar (SAR) system, which provides data all year and all-weather. In Sentinel-1 (S-1) time-series data, a forest clearing will lead to reduced backscatter intensity and increased interferometric SAR (InSAR) coherence magnitude. A time-series of 108 interferomtric wide (IW) Single look complex (SLC) S-1 images collected in 2016, 2017, and 2018 are used to study the potential for mapping clear-cut areas in eastern Ireland. We combined multitemporal InSAR coherence and backscatter intensity for the detection. This is an extension of previous studies that used either backscatter intensity or InSAR coherence magnitude, while we show the added value of both together. Coherence magnitude was the strongest predictor of the two.

In this article, we propose a determinant ratio test (DRT) statistic to measure the similarity of two covariance matrices for unsupervised change detection in polarimetric radar images. The multilook complex covariance matrix is assumed to follow a scaled complex Wishart distribution. In doing so, we provide the distribution of the DRT statistic that is exactly Wilks’s lambda of the second kind distribution, with density expressed in terms of Meijer G-functions. Due to this distribution, the constant false alarm rate (CFAR) algorithm is derived in order to achieve the required performance. More specifically, a threshold is provided by the CFAR to apply to the DRT statistic producing a binary change map. Finally, simulated and real multilook polarimetric SAR (PolSAR) data are employed to assess the performance of the method and is compared with the Hotelling–Lawley trace (HLT) statistic and the likelihood ratio test (LRT) statistic.

In this paper, we propose a new method for automatic change detection in multi-temporal fully polarimetric synthetic aperture radar (PolSAR) images based on multivariate statistical wavelet subband modeling. The proposed method allows us to take into account the correlation structure between subbands by modeling the wavelet coefficients through multi-variate probability distributions. Three types of correlation are investigated: inter-scale, inter-orientation, and inter-polarization dependences. The multivariate generalized Gaussian distribution (MGGD) is used to model the interdependencies between wavelet coefficients at different orientations, scales, and polarizations. Kullback-Leibler similarity measures are computed and used to generate the change map. Simulated and real multilook PolSAR data are employed to assess the performance of the method and are compared to the multivariate Gaussian distribution (MGD) based method. We show that the information embedded in the correlation between subbands improves the accuracy of the change map, leading to better performance. Moreover, the MGGD represents better the correlations between wavelet coefficients and outperforms the MGD.

The main objective of this paper to show the potential of multitemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) for detection of water hyacinth in Indian wetlands. Water hyacinth (Pontederia crassipes, also called Eichhornia crassipes) is one of the most destructive invasive weed species in many lakes and river systems worldwide, causing significant adverse economic and ecological impacts. We use the expectation maximization (EM) as a benchmark machine learning algorithm and compare its results with three supervised machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbour (kNN), using both synthetic aperture radar (SAR) and optical data to distinguish between clean and infested waters.

Iceberg properties, together with meteorological and environmental conditions can influence Synthetic Aperture Radar (SAR) backscatter behaviours. In this work, we used five images of quad-pol ALOS-2/PALSAR-2 SAR data to analyse icebergs in Greenland. We investigate the scattering mechanisms through several observables and decompositions. Our results show that the most common scattering mechanisms for icebergs is surface scattering and volume scattering. Sometimes double bounce is also observed. By performing a multi-scale analysis using boxcar$5 \times 5$and$11 \times 11$window sizes, we conclude that icebergs can be a collection of strong scatterers. This gives hope for using quad-pol polarimetry to provide some iceberg classifications in the future.

Icebergs represent hazards to ships and maritime activities and therefore their detection is essential. Synthetic Aperture Radar (SAR) satellites are very useful for this, due to their capability to acquire under cloud cover and during polar nights. Additionally, polarimetry has been proven to improve the detection capability. In this work, we compare six state-of-the-art quad polarimetric detectors to test their performance and ability to detect small sized icebergs in four locations in Greenland. These were the polarimetric notch filter (PNF), polarimetric match filter (PMF), polarimetric whitening filter (PWF), optimal polarimetric detector (OPD), reflection symmetry detector, and the dual polarisation anomaly detector (iDPolRAD). We use four single look complex ALOS-2 quad pol images. The data were calibrated and processed. We produce the covariance matrices of each image before applying a testing and training window for detection. We also add a guard window to reduce false alarms. Our results show that the multi-look polarimetric whitening filter and optimal polarimetric detector provide the most optimal performance in quad and dual pol mode detection.

Calving of icebergs at the tidewater glacier fronts is a component of the mass loss in Polar regions. Studying the regional distribution of icebergs, their volume, motion, and interaction with the environment is of interest. Here, we present the results from a fieldwork campaign conducted in Kongsfjorden, Svalbard in April 2016, where both satellite and ground-based remote sensing instruments were used to observe dynamics of sea ice, icebergs, and growlers. We used a ground-based radar system, imaging the study area every second minute during five days. During the same observation period, we collected four RADARSAT-2 (RS-2) quad-pol images, that are used for automatic detection of icebergs. In addition, the fieldwork team collected GPS positions of some drifting and grounded icebergs in the fjord to be used as ground-truth data. The comparison and combination of satellite, ground-based radar, and in-situ data contribute to cross-validate the results.

In the last decades, differential synthetic aperture radar (SAR) interferometric (InSAR) (DInSAR) techniques have been used to estimate the Earth's surface deformation with high resolution. In this paper, we present an approach for increasing the quantity of permanent scattered pixels. These pixels are selected for DInSAR processing based on polarimetric information prepared by new sensors. The objective of this paper is then to test existing algorithms that confirm the contribution of polarimetric data for improving persistent scatterers (PS) detection. These algorithms are formulated based on two different selection criteria: amplitude dispersion index and mean coherence. Different approaches are analyzed to optimize both selection criteria in terms of pixels' quantity and density and finally their results are quantitatively compared. Experimental results with exploiting quad-pol UAVSAR data set over an urban area in CA, provide the expected improvement. Comparing the number of PSs between quad-pol with dual-pol and single-pol cases illustrate remarkable improvement in both selection criteria. For quad-pol case, we achieve an increase of 50% and 60% with respect to dual-pol and single-pol data, respectively, when using average coherence and over 6 times more for amplitude dispersion index. The results of our study demonstrates the added value of polarimetric SAR observations (dual pol and quad-pol) for improved permanent scatterers detection monitored areas.

Icebergs can cause a significant threat to shipping, offshore oil and gas production facilities, and subsea pipelines. Synthetic aperture radar (SAR) is a well-established tool for detecting and monitoring sea-ice objects in the often dark and cloud-covered polar regions. However, detection of small icebergs floating in nonhomegeous sea clutter environments is still a challenging task. We propose a new methodology for automatic identification of potential icebergs in high-resolution polarimetric SAR images. The algorithm adopts to various sea-ice conditions and it tackles high iceberg density situations and heterogeneous background conditions in the marginal ice zone. Results from a time series of RADARSAT-2 data containing numerous icebergs broken off from glaciers in Kongsfjorden on Svalbard demonstrate that the approach is viable.

Icebergs can cause a significant danger for shipping, offshore oil exploration, and undersea or subsea pipelines and production facilities. Synthetic aperture radar (SAR) is very valuable tool of detecting and monitoring icebergs in the often dark and cloud-covered polar regions. Detection of small icebergs floating in nonhomegeous sea clutter environments is a challenging task in remote sensing. In this paper, a new methodology is proposed for automatic identification of icebergs in high-resolution polarimetric SAR images acquired during different seasons. This involves adapting the algorithm to sea-ice conditions, and facing challenges when it comes to high iceberg density, meteorological and oceanographic phenomena in the marginal ice zone causing heterogeneity in the background clutter. The algorithm is tested with time series of RADARSAT-2 C-band quad-polarimetric images to detect icebergs in Kongsfjorden (Ny-Ålesund, Svalbard) that have broken off from the glaciers nearby.

This paper is focused on investigations of the improved detection of icebergs in open water using spectral analysis of polarimetric SAR data. The objectives of this study are to analyze the behavior of ice objects in open water using the sublook cross-correlation magnitude (SCM). It is shown that there is an improvement of iceberg-sea contrast when the SCM is used instead of multilook intensities from the full-bandwidth data. The subband extraction in the azimuth dimension is used to test the stability of icebergs when they are observed by different azimuth view angles (i.e., looking fore or aft). Experiments are performed on full-polarimetric single-look complex (SLC) SAR data containing icebergs in open water. The results indicate that the sublook analysis has a impact on detection performance.

In this paper, we propose a new test statistic for unsupervised change detection in polarimetric radar images. We work with multilook complex covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex-kind Hotelling-Lawley trace (HLT) statistic for measuring the similarity of two covariance matrices. The distribution of the HLT statistic is approximated by a Fisher-Snedecor distribution, which is used to define the significance level of a false alarm rate regulated change detector. Experiments on simulated and real PolSAR data sets demonstrate that the proposed change detection method gives detection rates and error rates that are comparable with the generalized likelihood ratio test.

In this paper, we present an unsupervised change detection method for polarimetric synthetic aperture radar (Pol-SAR) images based on the relaxed Wishart distribution. Most polarimetric change detectors assume the Gaussian-based complex Wishart model for multilook covariance matrices, which is only satisfied for homogeneous areas with fully developed speckle and no texture. Liu et al. recently proposed a new change detection algorithm under the multilook product model (MPM) to describe the heterogeneous clutters. The improvement has come at the expense of higher computational cost since the similarity measure is based on more advanced models accounting for texture, and they contain some mathematical special functions that is difficult to evaluate such similarity measures. In this paper, we will demonstrate the ability of the relaxed Wishart distribution for textured change detection analysis. Change results on simulated and real data demonstrate the effectiveness of the algorithm.

In this paper, we propose a robust unsupervised change detection algorithm for multilook polarimetric synthetic aperture radar (PolSAR) data. The Hotelling-Lawley trace (HLT) statistic is used as a test statistic to measure the similarity of two covariance matrices. The generalized Kittler and Illingworth (K&I) minimum-error thresholding algorithm based on the generalized gamma function is then applied on the test statistic image to accurately discriminate changed and unchanged areas. Experiment on real PolSAR data set demonstrates the accuracy of the proposed change detection method.

This paper presents a processing chain for the change detection of Arctic glaciers from multitemporal multipolarization synthetic aperture radar (SAR) images. We produce terrain-corrected multilook complex covariance data by including the effects of topography on both geolocation and SAR radiometry as well as azimuth slope variations on polarization signature. An unsupervised contextual non-Gaussian clustering algorithm is employed for the segmentation of each terrain-corrected polarimetric SAR image and subsequently labeled with the aid of ground-truth data into glacier facies. We demonstrate the consistency of the segmentation algorithm by characterizing the expected random error level for different SAR acquisition conditions. This allows us to determine whether an observed variation is statistically significant and therefore can be used for the postclassification change detection of Arctic glaciers. Subsequently, the average classified images of succeeding years are compared, and changes are identified as the detected differences in the location of boundaries between glacier facies. In the current analysis, a series of dual-polarization C-band ENVISAT ASAR images over the Kongsvegen glacier, Svalbard, is used for demonstration.

In this paper we propose a new test statistic for unsupervised change detection in polarimetric synthetic aperture radar (Pol-SAR) data. We work with multilook complex (MLC) covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex kind Hotelling-Lawley (HL) trace statistic for measuring the similarity of two covariance matrices. The sampling distribution of the HL trace is approximated by a Fisher-Snedecor distribution, which is used to define the significance level of a constant false alarm rate change detector. The performance of the proposed method is tested on simulated and real PolSAR data sets and compared to the likelihood ratio test statistic.

This paper proposes a novel unsupervised, non-Gaussian, and contextual segmentation method that combines an advanced statistical distribution with spatial contextual information for multilook polarimetric synthetic aperture radar (PolSAR) data. This extends on previous studies that have shown the added value of both non-Gaussian modeling and contextual smoothing individually or for intensity channels only. The method is based on a Markov random field (MRF) model that integrates aK-Wishart distribution for the PolSAR data statistics conditioned to each image cluster and a Potts model for the spatial context. Specifically, the proposed algorithm is constructed based upon the stochastic expectation maximization (SEM) algorithm. A new formulation of SEM is developed to jointly perform clustering of the data and parameter estimation of theK-Wishart distribution and the MRF model. Experiments on simulated and real PolSAR data demonstrate the added value of using an appropriate statistical representation, in combination with contextual smoothing.

This paper investigates the impact of terrain correction on change detection results. We firstly assess the effects of topography on radar brightness and show how we can produce the radiometrically terrain corrected multilook complex (MLC) covariance data. Next, changes on radar cross section as a function of polarization states due to azimuth slope variations will be studied. Finally experimental results on glacier dataset are shown by focusing on change detection results before and after terrain corrections.

Synthetic Aperture Radar (SAR) satellite sensors recently provide valuable sources of earth observation data for various environmental applications. Beside the specifics properties of these data including multi-polarization and polarimetric image data, the presence of unavoidable speckle seriously degrades the quality of these data. Specifically, in certain applications such as clustering, classification and change detection speckles make some difficulties in analysis data and interpretation of results. In this research, a hybrid approach, based on frequency-domain transforms, is proposed. This method is a combination of wavelet and curvelet transforms to suppress the speckle noise in SAR images. This approach based on features and region which has a good efficiency in removing noise and preserving information of data in case of edges and shape. Results of these methods were compared simultaneously and with conventional speckle filtering methods (e.g. Lee, Frost and Kuan).

This paper proposes a novel speckle reduction method that combines an advanced statistical distribution with spatial contextual information for SAR data. The method for despeckling is based on a Markov random field (MRF) that integrates a K-distribution for the SAR data statistics and a Gauss-MRF model for the spatial context. These two pieces of information are combined based on weighted summation of pixel-wise and contextual models. This not only preserves edge information in the image, but also improves signal-to-noise ratio (SNR) of the despeckled data. Experiments on real SAR data demonstrate the effectiveness of the algorithm compared with well-known despeckling methods.

We present the application of a weighted least squares (WLS) method based on image mode interferometric data to monitor the spatiotemporal evolution of land surface subsidence in Mashhad valley, northeast Iran. The technique is based on an appropriate combination of differential interferograms produced by image pairs with small orbital separation to limit the spatial decorrelation phenomena. Our data consist of 17 ASAR single-look-complex images acquired from a descending orbit by the European ENVISAT satellite in image mode (I2), spanning a time interval from June 2004 to November 2007. Fifty-three reliable differential interferograms with relatively little noise and a continuous unwrapped phase are constructed from this data set and are analyzed using a WLS adjustment technique to produce time series of the displacement field. The time-series analysis suggests that the subsidence occurs within a northwest–southeast elongated elliptically shaped bowl along the axis of Mashhad valley. The maximum accumulated subsidence during the 1260-day period reaches approximately 86 cm, located northeast of Mashhad city. The comparison between SAR-interferometry time-series results with continuous Global Positioning System measurements yields an estimated root-mean-square error of$\sim$1.0 cm.

A clustering method that combines an advanced statistical distribution with spatial contextual information is proposed for multilook polarimetric synthetic aperture radar (PolSAR) data. It is based on a Markov random field (MRF) model that integrates a K-Wishart distribution for the PolSAR data statistics conditioned to each image cluster and a Potts model for the spatial context. Specifically, the proposed algorithm is constructed based upon the expectation maximization (EM) algorithm. A new formulation of EM is developed to jointly address parameter estimation in the K-Wishart distribution and the spatial context model, and also minimization of the energy function. Experiments are presented with simulated and real quad-pol L-band data.