Description: The use of synthetic aperture radar (SAR) technology with quad-polarization data requires efficient polarimetric SAR (PolSAR) speckle filtering algorithms. During the last three decades, many effective methods have been developed to reduce the speckle in PolSAR images, and recent studies have generally shown a trend developing from local single-point filtering to nonlocal patch-based or globally collaborative filtering. The main goals of this paper are to make a comprehensive review of the existing PolSAR despeckling algorithms and highlight the recent development trends. In the experimental part, the filtering results obtained with both simulated and real PolSAR images are deployed to compare the performance of some of the state-of-the-art despeckling algorithms, which shows that all of the selected filters have their individual strengths and weaknesses.

Description: This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing. FastHyDe and FastHyIn fully exploit extremely compact and sparse HSI representations linked with their low-rank and self-similarity characteristics. In a series of experiments with simulated and real data, the newly introduced FastHyDe and FastHyIn compete with the state-of-the-art methods, with much lower computational complexity.

Description: In the TanDEM-X mission, quad-polarization data (HH, HV, VH, and VV-polarization channels) can be acquired at an experimental basis by acquiring images in the dual-receive antenna (DRA) mode. This mode was activated during the so-called TanDEM-X science phase, from October 2014 up to January 2016, serving the science community with a unique dataset for the demonstration of new SAR techniques and applications. Quad-polarization data has been firstly acquired in pursuit monostatic mode and, secondly, in bistatic configuration as well. TanDEM-X is the first spaceborne mission which allows for the acquisition of quad-polarization data in bistatic formation, with across-track baselines varying up to 4 km at the Equator. The current work completes the one presented in [1] , where TanDEM-X quadpolarization data, acquired in pursuit monostatic mode only, was analyzed and recommendations were drawn, in order to optimize the acquisition parameters, aiming at improving the final data quality. Such recommendations were then taken into account for the acquisition of quad-polarization data in bistatic configuration, starting from April 2015, and the obtained results are presented in this paper. Investigations have been performed, aiming at monitoring the effective improvement in data quality. For example, we investigated the impact of different system parameters, such as noise equivalent sigma zero (NESZ) or processing bandwidth on the SAR performance, together with their influence on the interferometric SAR (InSAR) performance, assessed in terms of interferometric coherence and relative height error. Finally, we introduce and discuss an experimental acquisition mode, which allows to synthesize a quad-polarization product by combining two simultaneous dual-polarization acquisitions.

Description: Remote sensing air temperature mostly relies on linear algorithms that produce significantly variable results depending on various weather conditions. Recently, a novel nonlinear algorithm based on support vector machine (SVM) was reported with improved prediction accuracy by using multiple types of data including satellite and unmanned weather station, land coverage imagery, digital elevation model, astronomy, and calendar. To further improve the accuracy and consistence, this paper reports a selective arithmetic mean (SAM) approach for optimization of a previously reported SVM algorithm for area-wide near surface air temperature remote sensing using satellite and other types of data. Using Guangxi province as the study area, the results show that this new SAM approach significantly improved the overall retrieving quality over the previously reported simple arithmetic mean approach. The SAM approach has high tolerance to cloud, ground vegetation, and vertical and spatial spectrum variations, with superb prediction errors (absolute error, AE) and root mean square errors concentrated around 0.7 and 0.8 °C, respectively. The prediction error patterns with different atmosphere water content, enhanced vegetation index, and spatial spectrum were similar under all examined conditions. After SAM operations, the prediction error patterns showed a deep gap near a set error threshold ${boldsymbol delta} _{i}$ , especially near δ 0 (δ 0 ± 0.2) in every examined situation. SAM also produces significantly lower errors at AE ≥ δ 0 ≥ 0. The SVM model with SAM optimization minimizes the shortcomings of the classical temperature remote sensing technologies and is suitable for area-wide retrieving under natural conditions. Four modeli- g principles are summarized for building superb models.

Description: Quantification of tree canopy area and aboveground biomass is essential for monitoring ecosystems’ ecological functionalities, e.g., carbon sequestration and habitat provision. Miombo woodlands are vastly existent in developing countries that often lack resources to acquire LiDAR data or high spatiospectral resolution remote sensing data that have been proven to accurately estimate these structural attributes. This study explored the utility of freely available (via Google Maps) high (1-m) resolution red, green, and blue (RGB) satellite imagery in combination with object-based image analysis (OBIA) for estimating tree canopy area and aboveground biomass in Miombo woodlands. We randomly established 41 225-m 2 plots in Mukuvisi Woodland, Zimbabwe, and used RGB data with OBIA to estimate tree canopy area in those plots. We also field measured the height, canopy area, and trunk diameter at breast height of all trees that fell in those plots, then used the field data and a published allometric equation to estimate aboveground tree biomass (AGB). OBIA classification accuracy was high (Jaccard similarity index = 0.96). Data analysis showed significant positive linear relationship between AGB and field-measured canopy area $(R^{2} = {{0.87}}, p 〈 {{0.003}})$ , and significant exponential relationships between: 1) OBIA-derived canopy area and AGB $(R^{2} = {{0.56}}, p 〈 {{0.0001}})$ ; and 2) field-measured canopy area and OBIA-derived canopy area $(R^{2} = {{0.63}}, p 〈 {{0.0001}})$ , and no significant differences $(t = {{19.67}}, df = {{78}}, p = {{0.28}})$ between field-measured canopy are- ( $bar{ times } = 187.11,{{rm{m}}^2},sigma = 127.03$ ) and OBIA-derived canopy area ( $bar{ times } = 163.00,{{rm{m}}^2},sigma = 50.08$ ). We conclude that RGB data with OBIA are suitable for estimating tree canopy area in Miombo woodlands for various low-accuracy purposes (e.g., biomass estimation).

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: 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.

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: 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.

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.