Adaptive Multitemporal SAR Image Filtering Based on the Change Detection Matrix

5 pagesInternational audienceThis letter presents an adaptive filtering approach of synthetic aperture radar (SAR) image times series based on the analysis of the temporal evolution. First, change detection matrices (CDMs) containing information on changed and unchanged pixels are constructed for each spatial position over the time series by implementing coefficient of variation (CV) cross tests. Afterwards, the CDM provides for each pixel in each image, an adaptive spatiotemporal neighborhood which is used to derive the filtered value. The proposed approach is illustrated on a time series of 25 ascending TerraSAR-X images acquired from November 6, 2009 to September 25, 2011 over the Chamonix-MontBlanc test-site which includes different kinds of change such as parking occupation, glacier surface evolution, etc

Accurate Despeckling and Estimation of Polarimetric Features by Means of a Spatial Decorrelation of the Noise in Complex PolSAR Data

In this work, we extended a procedure for the spatial decorrelation of fully-developed speckle, originally developed for single-polarization SAR data, to fully-polarimetric SAR data. The spatial correlation of the noise depends on the tapering window in the Fourier domain used by the SAR processor to avoid defocusing of targets caused by Gibbs effects. Since each polarimetric channel is focused independently of the others, the noise-whitening procedure can be performed applying the decorrelation stage to each channel separately. Equivalently, the noise-whitening stage is applied to each element of the scattering matrix before any multilooking operation, either coherent or not, is performed. In order to evaluate the impact of a spatial decorrelation of the noise on the performance of polarimetric despeckling filters, we make use of simulated PolSAR data, having user-defined polarimetric features. We optionally introduce a spatial correlation of the noise in the simulated complex data by means of a 2D separable Hamming window in the Fourier domain. Then, we remove such a correlation by using the whitening procedure and compare the accuracy of both despeckling and polarimetric features estimation for the three following cases: uncorrelated, correlated, and decorrelated images. Simulation results showed a steady improvement of performance scores, most notably the equivalent number of looks (ENL), which increased after decorrelation and closely attained the value of the uncorrelated case. Besides ENL, the benefits of the noise decorrelation hold also for polarimetric features, whose estimation accuracy is diminished by the correlation. Also, the trends of simulations were confirmed by qualitative results of experiments carried out on a true Radarsat-2 image

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively

Multi-Objective CNN Based Algorithm for SAR Despeckling

Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is largely used in applications such as change detection, image restoration, segmentation, detection and classification. With reference to synthetic aperture radar (SAR) domain the application of DL techniques is not straightforward due to non trivial interpretation of SAR images, specially caused by the presence of speckle. Several deep learning solutions for SAR despeckling have been proposed in the last few years. Most of these solutions focus on the definition of different network architectures with similar cost functions not involving SAR image properties. In this paper, a convolutional neural network (CNN) with a multi-objective cost function taking care of spatial and statistical properties of the SAR image is proposed. This is achieved by the definition of a peculiar loss function obtained by the weighted combination of three different terms. Each of this term is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties and strong scatterers identification. Their combination allows to balance these effects. Moreover, a specifically designed architecture is proposed for effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared to the State-of-Art despeckling algorithms, both from quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for a correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous and extremely heterogeneous

Towards a 20m global building map from Sentinel-1 SAR Data

This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.Peer ReviewedPostprint (published version

Unsupervised multi-scale change detection from SAR imagery for monitoring natural and anthropogenic disasters

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Radar remote sensing can play a critical role in operational monitoring of natural and anthropogenic disasters. Despite its all-weather capabilities, and its high performance in mapping, and monitoring of change, the application of radar remote sensing in operational monitoring activities has been limited. This has largely been due to: (1) the historically high costs associated with obtaining radar data; (2) slow data processing, and delivery procedures; and (3) the limited temporal sampling that was provided by spaceborne radar-based satellites. Recent advances in the capabilities of spaceborne Synthetic Aperture Radar (SAR) sensors have developed an environment that now allows for SAR to make significant contributions to disaster monitoring. New SAR processing strategies that can take full advantage of these new sensor capabilities are currently being developed. Hence, with this PhD dissertation, I aim to: (i) investigate unsupervised change detection techniques that can reliably extract signatures from time series of SAR images, and provide the necessary flexibility for application to a variety of natural, and anthropogenic hazard situations; (ii) investigate effective methods to reduce the effects of speckle and other noise on change detection performance; (iii) automate change detection algorithms using probabilistic Bayesian inferencing; and (iv) ensure that the developed technology is applicable to current, and future SAR sensors to maximize temporal sampling of a hazardous event. This is achieved by developing new algorithms that rely on image amplitude information only, the sole image parameter that is available for every single SAR acquisition. The motivation and implementation of the change detection concept are described in detail in Chapter 3. In the same chapter, I demonstrated the technique's performance using synthetic data as well as a real-data application to map wildfire progression. I applied Radiometric Terrain Correction (RTC) to the data to increase the sampling frequency, while the developed multiscaledriven approach reliably identified changes embedded in largely stationary background scenes. With this technique, I was able to identify the extent of burn scars with high accuracy. I further applied the application of the change detection technology to oil spill mapping. The analysis highlights that the approach described in Chapter 3 can be applied to this drastically different change detection problem with only little modification. While the core of the change detection technique remained unchanged, I made modifications to the pre-processing step to enable change detection from scenes of continuously varying background. I introduced the Lipschitz regularity (LR) transformation as a technique to normalize the typically dynamic ocean surface, facilitating high performance oil spill detection independent of environmental conditions during image acquisition. For instance, I showed that LR processing reduces the sensitivity of change detection performance to variations in surface winds, which is a known limitation in oil spill detection from SAR. Finally, I applied the change detection technique to aufeis flood mapping along the Sagavanirktok River. Due to the complex nature of aufeis flooded areas, I substituted the resolution-preserving speckle filter used in Chapter 3 with curvelet filters. In addition to validating the performance of the change detection results, I also provide evidence of the wealth of information that can be extracted about aufeis flooding events once a time series of change detection information was extracted from SAR imagery. A summary of the developed change detection techniques is conducted and suggested future work is presented in Chapter 6

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Using Shadows to Detect Targets in Synthetic Aperture Radar Imagery

Synthetic Aperture Radar (SAR) can generate high resolution imagery of re- mote scenes by combining the phase information of multiple radar pulses along a given path. SAR based Intelligence, Surveillance, and Reconnaissance (ISR) has the advantage over optical ISR that it can provide usable imagery in adverse weather or nighttime conditions. Certain radar frequencies can even result in foliage or limited soil penetration, enabling imagery to be created of objects of interest that would otherwise be hidden from optical surveillance systems. This thesis demonstrates the capability of locating stationary targets of interest based on the locations of their shadows and the characteristics of pixel intensity distributions within the SAR imagery. Shadows, in SAR imagery, represent the absence of a detectable signal reflection due to the physical obstruction of the transmitted radar energy. An object\u27s shadow indicates its true geospatial location. This thesis demonstrates target detection based on shadow location using three types of target vehicles, each located in urban and rural clutter scenes, from the publicly available Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. The proposed distribution characterization method for detecting shadows demonstrates the capability of isolating distinct regions within SAR imagery and using the junctions between shadow and non-shadow regions to locate individual shadow-casting objects. Targets of interest are then located within that collection of objects with an average detection accuracy rate of 93%. The shadow-based target detection algorithm results in a lower false alarm rate compared to previous research conducted with the same data set, with 71% fewer false alarms for the same clutter region. Utilizing the absence of signal, in conjunction with surrounding signal reflections, provides accurate stationary target detection. This capability could greatly assist in track initialization or the location of otherwise obscured targets of interest

Kajian motivasi ekstrinsik di antara Pelajar Lepasan Sijil dan Diploma Politeknik Jabatan Kejuruteraan Awam KUiTTHO

Kajian ini dijalankan untuk menyelidiki pengaruh dorongan keluarga, cara pengajaran pensyarah, pengaruh rakan sebaya dan kemudahan infrastruktur terhadap motivasi ekstrinsik bagi pelajar tahun tiga dan tahun empat lepasan sijil dan diploma politeknik Jabatan Kejuruteraan Awain Kolej Universiti Teknologi Tun Hussein Onn. Sampel kajian ini beijumlah 87 orang bagi pelajar lepasan sijil politeknik dan 38 orang bagi lepasan diploma politeknik. Data kajian telah diperolehi melalui borang soal selidik dan telah dianalisis menggunakan perisian SPSS (Statical Package For Sciences). Hasil kajian telah dipersembahkan dalam bentuk jadual dan histohgrapi. Analisis kajian mendapati bahawa kedua-dua kumpulan setuju bahawa faktor-faktor di atas memberi kesan kepada motivasi ekstrinsik mereka. Dengan kata lain faktpr-faktor tersebut penting dalam membentuk pelajar mencapai kecemerlangan akademik

Classification of Pre-Filtered Multichannel Remote Sensing Images

Open acces: http://www.intechopen.com/books/remote-sensing-advanced-techniques-and-platforms/classification-of-pre-filtered-multichanel-rs-imagesInternational audienc