Description: 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.

Description: 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.

Description: 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.

Description: 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.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multispectral (MS) images. As the transformation from low spatial resolution MS image to high-resolution MS image is complex and highly nonlinear, inspired by the powerful representation for nonlinear relationships of deep neural networks, we introduce multiscale feature extraction and residual learning into the basic convolutional neural network (CNN) architecture and propose the multiscale and multidepth CNN for the pan-sharpening of remote sensing imagery. Both the quantitative assessment results and the visual assessment confirm that the proposed network yields high-resolution MS images that are superior to the images produced by the compared state-of-the-art methods.

Description: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.

Description: A novel method that combines joint clusters and iterative graph cuts for ALS point cloud filtering is proposed in this paper. The method first extracts clusters of points from the raw point cloud, and then classifies ground points at the cluster level. There are four main steps, i.e., two-step point cloud clustering, critical point extraction, initial terrain determination, and terrain densification based on iterative graph cuts. Smooth clusters, rough clusters, and scattered points are extracted by the two-step clustering to depict the raw point cloud, which reduces the complexity of raw data and the judgment difficulty in the subsequent procedures. Critical points of each cluster are extracted, and the initial terrain is determined among the smooth clusters. Using the initial terrain and critical points, iterative graph cuts is performed to segment ground and nonground points at the cluster level. Experiments on ISPRS dataset with a low point density and Utah dataset with a moderate point density show that our approach provides a satisfactory trade off between Type I and Type II errors. Moreover, our method significantly outperforms progressive TIN densification based filters, and successfully controls Type II errors.

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Description: Compared with color or grayscale images, hyperspectral images deliver more informative representation of ground objects and enhance the performance of many recognition and classification applications. However, hyperspectral images are normally corrupted by various types of noises, which have a serious impact on the subsequent image processing tasks. In this paper, we propose a novel hyperspectral image denoising method based on tucker decomposition to model the nonlocal similarity across the spatial domain and global similarity along the spectral domain. In this method, 3-D full band patches extracted from a hyperspectral image are grouped to form a third-order tensor by utilizing the nonlocal similarity in a proper window size. In this way, the task of image denoising is transformed into a high-order tensor approximation problem, which can be solved by nonnegative tucker decomposition. Instead of a traditional alternative least square based tucker decomposition, we propose a hierarchical least square based nonnegative tucker decomposition method to reduce the computational cost and improve the denoising effect. In addition, an iterative denoising strategy is adopted to achieve better denoising outcome in practice. Experimental results on three datasets show that the proposed method outperforms several state-of-the-art methods and significantly enhances the quality of the corrupted hyperspectral image.