Description: Hyperspectral images (HSIs) are usually contaminated by various kinds of noise, such as stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their subsequent application. In this paper, we model the stripes, deadlines, and impulse noise as sparse noise, and propose a unified mixed Gaussian noise and sparse noise removal framework named spatial–spectral total variation regularized local low-rank matrix recovery (LLRSSTV). The HSI is first divided into local overlapping patches, and rank-constrained low-rank matrix recovery is adopted to effectively separate the low-rank clean HSI patches from the sparse noise. Differing from the previous low-rank-based HSI denoising approaches, which process all the patches individually, a global spatial–spectral total variation regularized image reconstruction strategy is utilized to ensure the global spatial–spectral smoothness of the reconstructed image from the low-rank patches. In return, the globally reconstructed HSI further promotes the separation of the local low-rank components from the sparse noise. An augmented Lagrange multiplier method is adopted to solve the proposed LLRSSTV model, which simultaneously explores both the local low-rank property and the global spatial–spectral smoothness of the HSI. Both simulated and real HSI experiments were conducted to illustrate the advantage of the proposed method in HSI denoising, from visual/quantitative evaluations and time cost.

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: Synthetic aperture radar (SAR) images display very high dynamic ranges. Man-made structures (like buildings or power towers) produce echoes that are several orders of magnitude stronger than echoes from diffusing areas (vegetated areas) or from smooth surfaces (e.g., roads). The impulse response of the SAR imaging system is, thus, clearly visible around the strongest targets: sidelobes spread over several pixels, masking the much weaker echoes from the background. To reduce the sidelobes of the impulse response, images are generally spectrally apodized, trading resolution for a reduction of the sidelobes. This apodization procedure (global or shift-variant) introduces spatial correlations in the speckle-dominated areas that complicates the design of estimation methods. This paper describes strategies to cancel sidelobes around point-like targets while preserving the spatial resolution and the statistics of speckle-dominated areas. An irregular sampling grid is built to compensate the subpixel shifts and turn cardinal sines into discrete Diracs. A statistically grounded approach for point-like target extraction is also introduced, thereby providing a decomposition of a single look complex image into two components: a speckle-dominated image and the point-like targets. This decomposition can be exploited to produce images with improved quality (full resolution and suppressed sidelobes) suitable both for visual inspection and further processing (multitemporal analysis, despeckling, interferometry).

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: The Sentinel-1 GRD (ground range detected) Level-1 product generated by the Instrument Processing Facility of the European Space Agency has noise artifacts at the image borders, which are quite consistent at both left and right sides of the satellite's cross track and at the start and end of the data take along track. The Sentinel-1 border noise troubles the creation of clean and consistence time series of backscatter. Data quality control and management become very challenging tasks, when it comes to the large-scale data processing, both in terms of spatial coverage and data volume. In this paper, we evaluate three techniques for removing the Sentinel-1 border noise and compare the results with the existing “ Sentinel-1 GRD Border Noise Removal ” algorithm implemented in the Sentinel-1 toolbox of the Sentinel application platform. 1 Validation and evaluation of the newly proposed algorithms was done using random samples containing 1500 Sentinel-1 scenes selected from a complete Sentinel-1 archive. The newly proposed approach has successfully achieved the required level of accuracy and solved the issue of time-series anomalies due to the border noise.

Description: The normalized difference vegetation index (NDVI) has been widely used in recent decades to monitor vegetation phenology. However, interference from snow cover introduces a high degree of uncertainty in interpreting NDVI fluctuation, because snow melting increases NDVI value in a manner similar to vegetation growth, leading to false detection. In this study, we present a novel methodology to smooth out data noise caused by snow in the third generation NDVI dataset from Global Inventory Modeling and Mapping Studies (GIMMS NDVI3g). This method is developed to replace small values with a pixel-specific snow-free background NDVI estimate, based on the assumption that the existence of snow decrease NDVI value and the patterns of NDVI fluctuation after snow melting and that after initiation of vegetation growth are different. Using the daily gross primary production (GPP) data of 111 site-years from FLUXNET in nine North American sites and the GIMMS NDVI3g dataset, we found that the green-up onset day (GUD) derived from raw NDVI is 42.2 days earlier than that of GPP, on average. This difference decreases to 4.7 days when applying the newly developed method. Additionally, the root mean square error and Spearman's correlation coefficient between NDVI-derived GUD and GPP-derived GUD are improved from 46.8 to 12.8 days and 0.22 to 0.64, respectively. Our results indicate that this method could effectively improve the ability to monitor the vegetation phenology by NDVI time series in areas with seasonal snow cover.

Description: We propose a novel spatiotemporal fusion method based on deep convolutional neural networks (CNNs) under the application background of massive remote sensing data. In the training stage, we build two five-layer CNNs to deal with the problems of complicated correspondence and large spatial resolution gaps between MODIS and Landsat images. Specifically, we first learn a nonlinear mapping CNN between MODIS and low-spatial-resolution (LSR) Landsat images and then learn a super-resolution CNN between LSR Landsat and original Landsat images. In the prediction stage, instead of directly taking the outputs of CNNs as the fusion result, we design a fusion model consisting of high-pass modulation and a weighting strategy to make full use of the information in prior images. Specifically, we first map the input MODIS images to transitional images via the learned nonlinear mapping CNN and further improve the transitional images to LSR Landsat images via the fusion model; then, via the learned SR CNN, the LSR Landsat images are supersolved to transitional images, which are further improved to Landsat images via the fusion model. Compared with the previous learning-based fusion methods, mainly referring to the sparse-representation-based methods, our CNNs-based spatiotemporal method has the following advantages: 1) automatically extracting effective image features; 2) learning an end-to-end mapping between MODIS and LSR Landsat images; and 3) generating more favorable fusion results. To examine the performance of the proposed fusion method, we conduct experiments on two representative Landsat–MODIS datasets by comparing with the sparse-representation-based spatiotemporal fusion model. The quantitative evaluations on all possible prediction dates and the comparison of fusion results on one key date in both visual effect and quantitative evaluations demonstrate that the proposed method can generate more accurate fusion results.

Description: Accurately monitoring forest dynamics in the tropical regions is essential for ecological studies and forest management. In this study, images from phase-array L-band synthetic aperture radar (PALSAR), PALSAR-2, and Landsat in 2006–2010 and 2015 were combined to identify tropical forest dynamics on Hainan Island, China. Annual forest maps were first mapped from PALSAR and PALSAR-2 images using structural metrics. Those pixels with a high biomass of sugarcane or banana, which are widely distributed in the tropics and subtropics and have similar structural metrics as forests, were excluded from the SAR-based forest maps by using phenological metrics from time series Landsat imagery. The optical–SAR-based forest maps in 2010 and 2015 had high overall accuracies (OA) of 92–97% when validated with ground reference data. The resultant forest map in 2010 shows good spatial agreement with public optical-based forest maps (OA = 88–90%), and the annual forest maps (2007–2010) were spatiotemporally consistent and more accurate than the PALSAR-based forest map from the Japan Aerospace Exploration Agency (OA = 82% in 2010). The areas of forest gain, loss, and net change on Hainan Island from 2007 to 2015 were 415 000 ha (+2.17% yr –1 ), 179 000 ha (–0.94% yr –1 ), and 236 000 ha (+1.23% yr –1 ), respectively. About 95% of forest gain and loss occurred in those areas with an elevation less than 400 m, where deciduous rubber, eucalyptus plantations, and urbanization expanded rapidly. This study demonstrates the potential of- PALSAR/PALSAR-2/Landsat image fusion for monitoring annual forest dynamics in the tropical regions.

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.