Beschreibung: The fuzzy c-means (FCM) algorithm and many improved algorithms incorporating spatial information have been proven to be effective in image segmentation. However, these methods are not adaptable to process synthetic aperture radar (SAR) images owing to the intrinsic speckle noise. Our solution, which enables the effective segmentation of SAR images by guaranteeing noise-immunity and edge detail preservation simultaneously, is to propose a robust FCM algorithm based on Bayesian nonlocal spatial information (RFCM $_$ BNL). The nonlocal idea considers more useful information for generating an auxiliary image. We measure the similarity between patches by utilizing a dedicated noise model for SAR images, and then apply it to the Bayesian formulation. Then we derive a new statistical distance, which is insensitive to speckle noise. Additionally, we ensure that the algorithm is robust to outliers by employing the entropy of the local gray-level histogram to control the extent to which the nonlocal spatial information term is adaptive to pixels. Experiments on simulated and real SAR images show that RFCM $_$ BNL obtains the best result for SAR image segmentation compared with seven other fuzzy clustering algorithms.

Beschreibung: Continuous monitoring of topographic heights and changes in tidal flats is challenging, as it is generally difficult to observe topographic changes from on-site measurements or remote sensing techniques with high resolution and high accuracy. In this regard, an interferometric synthetic aperture radar (In-SAR) can be an effective tool to generate precise digital elevation models (DEMs) and detect large-scale topographic changes. Nevertheless, utilizing the In-SAR to detect topographic changes in tidal flats is not practical because the average slope of tidal flats is usually less than 5°, and the overall spatial and temporal variations of height are not significant. Therefore, the accuracy of In-SAR DEMs must be high to detect meaningful topographic changes. In order to minimize the error of In-SAR DEMs, height of ambiguity and random phase deviation of interferograms should be taken into account. These two factors are related to incidence angle and baseline. We simulated topographic error levels in tidal flats for a single-pass In-SAR system such as TanDEM-X. Phase error of interferograms was derived using the relationship between In-SAR coherence and the probability density function of phase deviation. Signal-to-noise ratio and geometric decorrelation were formulated by the function of baseline, incidence angle, and surface slope. The simulation results show that the height error of the DEM was minimized to lower than 15 cm when the baseline was 1500 m with an incidence angle of 29° in the TanDEM-X system. Finally, the validation of simulation results was carried out by comparing them with TanDEM-X DEM accuracies in tidal flats.

Beschreibung: Multilooking is a key step in interferometric processing, especially in so-called coherent stacking interferometry approaches. In the past, multilooking algorithms were mainly implemented in the spatial domain on single interferometric pairs. With continuous development in repeat pass capabilities, multitemporal coherent synthetic aperture radar (SAR) images are now generally acquired more easily, thus providing the possibility to exploit also the temporal signature for multilooking. In this field, the possibility to carry out adaptive multilooking is fundamental for the improvement of interferometric processing. A basic similarity test has been introduced in the SqueeSAR approach, namely the Kolmogorov–Smirnov test. Furthermore, similarity tests have been discussed in terms of real-valued data vector, so only amplitude information can be utilized: The influence of the phase signal is typically ignored. To fully exploit the complex information acquired by coherent SAR systems, this paper proposes an adaptive multilooking algorithm based on complex patch (AMCP). The complex signal, which is fundamental in interferometric systems, is here exploited for the derivation of a new patch selection method. The AMCP algorithm can be applicable to all multitemporal techniques that need filtering, including the InSAR stacks of single main image and multi main images. Experiments on simulated data and real data validate that the proposed algorithm has the highlighting major advantages in improving measurement precision compared with traditional methods.

Beschreibung: Tea is an important cash crop in Kenya, grown in a climatically restricted geographic area where climatic variability is starting to affect yield productivity levels. This paper assesses the feasibility of monitoring tea growth between, but also within fields, using X-band COSMO-SkyMed SAR images (five images at VV polarization and five images at HH polarization). We detect the harvested and nonharvested areas for each field, based on the loss of interferometric coherence between two images, with an accuracy of 52% at VV polarization and 74% at HH polarization. We then implement a normalization method to isolate the scattering component related to shoot growth and eliminate the effects of moisture and local incidence angle. After normalization, we analyze the difference in backscatter between harvested and nonharvested areas. At HH polarization, our backscatter normalization reveals a small decrease ( $sim0.1$  dB) in HH backscatter after harvest. However, this decrease is too small for monitoring shoot growth. The decrease is not clear at VV polarization. This is attributed to the predominantly horizontal orientation of the harvested leaves.

Beschreibung: This paper addresses the issue of deceptive jamming against synthetic aperture radar (SAR) by using 1-bit sampling and time-varying threshold (TVT). With 1-bit intercepted SAR signal, the multipliers involved in a convolution is replaced by xnor gates, which considerably simplify the jamming signal generation. Moreover, the TVT is used for 1-bit quantization before retransmission to retain the relative amplitude information of the jamming signal. As a result, the proposed deceptive jamming schemes are superior to their conventional counterpart in terms of realization. Effects of harmonics and oversampling are analyzed to evaluate the performance degradations caused by the 1-bit sampling and TVT. Simulation results are provided to confirm the validity of the proposed schemes.

Beschreibung: The parameters used for passive soil moisture retrieval algorithms reported in the literature encompass a wide range, leading to a large uncertainty in the applicability of those values. This paper presents an evaluation of the proposed parameterizations of the tau–omega model from 1) the soil moisture active passive (SMAP) algorithm theoretical basis document (ATBD) for global condition and 2) calibrated parameters from the National Airborne Field Experiment (NAFE’05) for Australian conditions, with special focus on the vegetation parameter b and roughness parameter $H_{R}$ . This study uses airborne L -band data and field observations from the SMAP experiments conducted in south-eastern Australia. Results show that the accuracy with the proposed parameterizations from SMAP ATBD was satisfactory at 100-m spatial resolution for maize (0.07 m 3 /m 3 ) and pasture (0.07 m 3 /m 3 ), while it decreased to 0.19 m 3 /m 3 for wheat. Calibrated parameters from the NAFE’05 did not provide better results, with the accuracy of wheat degrading to 0.23 m 3 /m 3 . After a comprehensive site-specific calibration and validation at 100-m spatial resolution, this result was improved to 0.10 m 3 /m 3 . Further calibration and validation were performed at 1-km resolution against intensive ground sampling and at 3-km against in situ monitoring stations. Results showed an accuracy over grassland and cropland of 0.04 m 3 /m 3 and 0.05 m 3 /m 3 , respectively. This study also suggests that the paramet- rs from SMAP ATBD show an underestimation of soil moisture, with the roughness parameter $H_{R}$ being too low for south-eastern Australian condition. Therefore, a new set of b and $H_{R}$ parameters for ten different land cover types was proposed in this study.

Beschreibung: The linear operator has been widely used to detect targets of interest in multispectral/hyperspectral images, and is usually able to achieve good performance when the target is linearly separable from the background. However, when dealing with a complex scene, it is hard to find a single projection direction, along which the target can be well distinguished from all the background objects. Therefore, we propose a piecewise linear strategy (PLS) for target detection, which is based on the assumption that the desired target is generally linearly separable from each background object. PLS first divides the whole background into several partitions, and then detects the individual target for each partition by using a commonly used linear detector. Experiments with simulated and real-world multispectral/hyperspectral images show that PLS can acquire a nonlinear detection result and can outperform state-of-the-art target detection operators.

Beschreibung: Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and hamper the potential impact of machine learning classifiers. This paper investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We present a large-scale human settlement mapping workflow based-off multiple modules to adapt a pretrained CNN to address the negative impact of distribution shift on classification performance. To extend a locally trained classifier onto large spatial extents areas we introduce several submodules: First, a human-in-the-loop element for relabeling of misclassified target domain samples to generate representative examples for model adaptation; second, an efficient hashing module to minimize redundancy and noisy samples from the mass-selected examples; and third, a novel relevance ranking module to minimize the dominance of source example on the target domain. The workflow presents a novel and practical approach to achieve large-scale domain adaptation with binary classifiers that are based-off CNN features. Experimental evaluations are conducted on areas of interest that encompass various image characteristics, including multisensors, multitemporal, and multiangular conditions. Domain adaptation is assessed on source–target pairs through the transfer loss and transfer ratio metrics to illustrate the utility of the workflow.

Beschreibung: We propose a high-resolution velocity analysis method to estimate the electromagnetic wave propagation velocity in subsurface medium. The estimation is achieved by applying the ℓ-1 norm regularized least-squares method to the conventional common-midpoint (CMP) velocity analysis algorithm. The proposed method can provide not only higher resolution than the conventional velocity analysis method, but can also be applied with a coarse sampling array system, such as our array ground penetrating radar YAKUMO, which returns eight CMP traces within a two meter width. The main purpose of this approach is for precise pavement inspection at shallow depths. We applied this method to both a simulated dataset and real data acquired by YAKUMO at a model airport taxiway to detect the slight velocity changes caused by millimeter-thin cracks filled with air or water within the 15 cm-thick asphalt pavement. In both cases slight velocity changes of about 0.005 m/ns can be detected, and the difference between air- and water-filled cracks can be distinguished. Also, this method is applied to a data acquired at airport taxi-way, the damaged parts are detected successfully and shows good agreement with the corning results. The results indicate that the proposed method is effective for pavement inspection, especially in the presence of thin cracks that cannot be seen directly with the reflected signal.

Beschreibung: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.