An object-based sparse representation model for spatiotemporal image fusion

Lake surface water temperature (LSWT) is an important factor of water ecological environment. As global warming, LSWT is also on the rise. Research on the main reasons of LSWT rising is the basis for controlling and improving the regional ecological environment. However, it is difficult for the existing remote sensing images to take into account the temporal and spatial resolution. Low-resolution images have a serious impact on data accuracy and even produce incorrect results. Therefore, obtaining high temporal and spatial resolution images by downscaling is of great significance to more accurately analyze the temporal and spatial characteristics of LSWT. In this article, Dianchi Lake is selected as research area, and the high spatial resolution image (Landsat) and high temporal resolution image (MODIS) are taken as data. Based on the downscaling algorithm of statistics and learning, DisTrad– super-resolution convolutional neural network (SRCNN) downscaling model is proposed, and the monthly average dataset of LSWT with 50 m resolution is constructed. The results showed 1) DisTrad–SRCNN can reflect the most distribution characteristics of LSWT (SSIMday = 0.96, PSNRday = 23.97; SSIMnight = 0.95, PSNRnight = 24.99). 2) LSWT had an overall upward trend (CRday = 0.22 °C/10a, CRnight = 0.21 °C/10a), showing a cyclical change of cold–warm–cold about 4 years. 3) The northern and lakeshore area were basically in the high temperature, and the whole lake presents a 4–5-year warm–cold–warm periodic change; the LSWT closer to the urban and residential areas and its change rate were relatively high, which indirectly reflected the serious impact of human activities on LSWT.

Colored satellite images are difficult to segment due to their low illumination, dense features, uncertainties, etc. Rényi’s entropy is a famous entropy criterion that provides excellent outputs in bi-level thresholding based segmentation. But such method suffers lack of accuracy, inefficiency, and instability when extended to perform color image multilevel thresholding. Therefore, a new color image multilevel segmentation strategy based on Bat algorithm and Rényi’s entropy is proposed in this paper to determine the optimal threshold values more efficiently. The experiments are conducted on four real satellite images and two well-known test images at different threshold levels. The study shows that the proposed algorithm obtains good quality and adequate segmented results more effectively as compared to other multilevel thresholding algorithms such as Rényi’s-PSO and Otsu-PSO.

To solve the dense matching problem for stereoscopic satellite images, a hybrid matching scheme integrating multiple methods is proposed. This scheme utilizes two types of matching element including grid points and edge points. First the geometrically constrained cross-correlation (GC3) method is used to extract matching grids. While in less textured regions using GC3 cannot get sufficient matching grids, so the local affine transformation is used to establish the region correspondence, and more matching grids can be generated. Edges are extracted by Canny operator and approximated with a series of straight edge segments using a polygon approximation. Based on these approximated edges, edge correspondences between image pairs are established using GC3. This scheme fuses the region based and the feature based matching methods. It has been tested with real satellite images and the results demonstrate its accuracy and efficiency.

Remote sensing satellite images are used widely in space imaging applications by acquiring significant information of earth surface. The size of these images is typically huge in amount and they need to transmit to the ground for a specific application. Thus, the efficient compression techniques are required to provide better bandwidth utilization for reducing transmission time. Since the images are actually strongly correlated spatially, spectrally and temporally, these correlations give ample opportunities to compress the data in various domains. In addition, the data features have a strong similarity in the disjoint spectral regions. Consequently, the similarity measurement based band reordering strategy is used for increasing the compression performance. However, the optimal band reordering is still a computationally challenging problem. In this paper, a correlation based heuristic has been proposed for the band reordering along with the recommended standard by Consultative Committee for Space Data Systems (CCSDS) 123 lossless encoder for better compression. The experimental result with real satellite images gives around 4.5-6.5% higher compression ratio than that of without reordering.

Several earth observation satellites acquire image bands with different spatial resolutions, e.g., a panchromatic band with high resolution and spectral bands with lower resolution. Likewise, we often face the problem of different resolutions when performing joint analysis of images acquired by different satellites. This work presents models and methods for classification of multiresolution images. The approach is based on the concept of a reference resolution, corresponding to the highest resolution in the dataset. Prior knowledge about the spatial characteristics of the classes is specified through a Markov random field model at the reference resolution. Data at coarser scales are modeled as mixed pixels by relating the observations to the classes at the reference resolution. A Bayesian framework for classification based on this multiscale model is proposed. The classification is realized by an iterative conditional modes (ICM) algorithm. The parameter estimation can be based both on a training set and on pixels with unknown class. A computationally efficient scheme based on a combination of the ICM and the expectation-maximization algorithm is proposed. Results obtained on simulated and real satellite images are presented.

ABSTRACTSuper-resolution mapping (SRM) is a technique for exploring spatial distribution information of the land-cover classes at finer spatial resolution. The soft-then-hard super-resolution mapping (STHSRM) algorithm is a type of SRM algorithm that first estimates the soft class values for sub-pixels at the target fine spatial resolution and then predicts the hard class labels for sub-pixels. The sub-pixel shifted images from the same area can be incorporated to improve the accuracy of STHSRM algorithm. In this article, multiscale sub-pixel shifted images (MSSI) based on the fine-scale model and the coarse-scale model are utilized to increase the accuracy of STHSRM. First, class fraction images are derived from multiple sub-pixel shifted coarse spatial resolution images by soft classification. Then using the sub-pixel/sub-pixel spatial attraction model as fine-scale and the sub-pixel/pixel spatial attraction model as coarse scale, all MSSI can be derived from fraction images. The MSSI for each class are then integrated to obtain the desired fine spatial resolution images. Finally, the integrated fine spatial resolution images are used to allocate classes for sub-pixel. Experiments on two synthetic remote sensing images and a real hyperspectral remote sensing imagery show that the proposed method produces higher mapping accuracy result.

Super-resolution (SR) has attracted increasing attention as it can improve the quality of video satellite images. Most previous studies only consider several integer magnification factors and focus on obtaining a specific SR model for each scale factor. However, in the real world, it is a common requirement to zoom the videos arbitrarily by rolling the mouse wheel. In this article, we propose a unified network for arbitrary scale SR (ASSR) of video satellite images. The proposed ASSR consists of two modules, i.e., feature learning module and arbitrary upscale module. The feature learning module accepts multiple low-resolution (LR) frames and extracts useful features of those frames by using many 3-D residual blocks. The arbitrary upscale module takes the extracted features as input and enhances the spatial resolution by subpixel convolution and bicubic-based adjustment. Different from existing video satellite image SR methods, ASSR can continuously zoom LR video satellite images with arbitrary integer and noninteger scale factors in a single model. Experiments have been conducted on real video satellite images acquired by Jilin-1 and OVS-1. Quantitative and qualitative results have demonstrated that ASSR has superior reconstruction performance compared with the state-of-the-art SR methods.

Tracking of cloud characteristics helps in detecting cyclones which accounts for disastrous winds and heavy landfall on low lying coastal areas. Dense Motion Estimation is one of the most computationally intensive part of any tracking system. In this work we propose an efficient and high throughput hardware implementation of the Dense Horn-Schunck(DHSOF) algorithm to accelerate the cloud motion analysis software running on host PC. A Xilinx Virtex-7 FPGA interfaced to host PC via Peripheral Component Interconnect Express(PCIe) acts as hardware accelerator. The DHSOF architecture can process 3750×3750 resolution images in real-time (30fps), which is highest among the state of art methods in the literature. An analysis from the subsystem level to the whole design is performed to find optimal bit width for each stage. Results are verified using standard Middlebury dataset and validated using real world GOES-14 satellite images.

Wetland mapping by fusing fine spatial and hyperspectral resolution images

Neutron scattering facilities like those available at ORNL are enabling the discovery and development of advanced materials include those to be used for next generation energy storage systems. In spite of all the success that neutron imaging has enjoyed in recent years, even higher-impact research is limited by the current temporal and spatial resolution of neutron detection devices. In particular, the spatial resolution of all neutron imaging methods in use and under development is fundamentally limited by the variance introduced by the charged particles emitted from neutron absorption. Thus, we are investigating, designing and demonstrating an array of ~1 micron-sized glass scintillating fibers arranged via photonics-crystal-fiber patterning techniques-- with particle tracking (a must) so that position resolution can be finer than than that allowed by the charged particle ranges. The processes, knowledge and algorithms developed will allow for spatially (1 micron) and time resolved (<100 ns), high throughput neutron transmission imaging in large areas at reasonable costs.

Future space-borne synthetic aperture radar, hyper-spectral imaging, and high spatial and temporal resolution imaging will place increasing demands on space network data throughput. It is expected that space data relay systems will require capacities of multiple Terabits per second (Tbps). Free-space optical communication is likely the only technology capable of supporting these data demands. A previous study1 has shown that implementing these high-capacity optical feeder links through a traditional geostationary constellation will pose significant development challenges. Meshed satellite constellations in low earth orbit (LEO), operating at significantly shorter ranges, were shown to have the potential to support Tbps feeder links using the technical capabilities of current free-space optical communication systems. Meshed LEO constellations, however, provide unique challenges, including complex constellation maintenance, dynamic meshing and data routing, and short contact periods with ground stations. We review the design requirements for high capacity LEO relay optical terminals and satellites. The global coverage and ground station downlink opportunities of constellation options are evaluated to develop constellation designs utilizing the minimum number of satellites while providing required feeder link performance. The constellations are designed to provide mesh links with Tbps feeder link capabilities using optical terminals comprised of components within current state-of-the-art and with demonstrated capabilities. These meshed feeder link constellations were then modelled to demonstrate the ability of a LEO constellation to gather Tbps of data over a global network, relay this information around the constellation, and downlink this data to a limited number of ground stations.

Magnetic resonance imaging (MRI) technology and availability has rapidly improved over recent years with advances in magnet and gradient design, phased array coils and software that permit high spatial and temporal resolution imaging in a clinically practical scan time. MRI combined with MR cholangiopancreatography (MRCP) provides anatomic, physiologic and potentially biochemical information without the need for ionizing radiation or iodinated contrast agents. Compared to computed tomography (CT), MRI provides superior soft tissue contrast, direct multiplanar capability and three-dimensional (3D) data acquisition that is particularly beneficial for pancreatic imaging. Although CT has traditionally been the gold standard for imaging in acute pancreatitis, MRI is a reliable alternative for staging, assessing severity and predicting outcome in patients with acute pancreatitis (; ; ; ; ).

Unmanned Aerial Vehicles (UAVs), can be controlled automatically or with the help of a remote control system, provide high spatial and temporal resolution images in addition to its advantages in terms of cost and time. Due to these features, UAV’s have been widely used in studies such as map production, 3D modeling and volume calculation in recent years. In this study, it is aimed to investigate the effects of different flight parameters on the accuracy of Orthomosaic and DEM produced with UAV Systems. In addition, the rolling shutter effect and the use of pre-calibrated and automatic camera calibration parameters were also tested. For this purpose, a total of seven UAV flights were carried out at different heights, overlap ratios, angles and grids at a mining area located within the borders of Aksaray province. Agisoft MetaShape and Pix4D Mapper, which are widely used commercial software, were used to process the UAV images. In this study, 27 points were established and 3D coordinates of these points were measured using GNSS technique. 15 of these points were used as reference points and the remaining 12 points were used as check points. Accuracy analysis was performed by comparing the produced Orthomosaics and DEMs with the check points in the study area. CloudCompare and ArcGIS software were used in the comparison of the products as a result of the evaluation of the images. It is concluded that the Orthomosaics produced using 450 angle and DEMs with 600 angle has higher accuracy than the others. Point clouds and DEMs’ produced using automatic and pre-calibrated camera calibration parameters show that the pre-calibrated images provide higher accuracy. Also, it is seen that when the rolling shutter effect is modeled, the horizontal and vertical accuracy is increased in all three flights with different flight parameters in this study.

The formation dynamics of periodic ripples induced by femtosecond laser pulses (pulse duration τ = 50 fs and central wavelength λ = 800 nm) are studied by a collinear pump-probe imaging technique with a temporal resolution of 1 ps and a spatial resolution of 440 nm. The ripples with periods close to the laser wavelength begin to appear upon irradiation of two pump pulses at surface defects produced by the prior one. The rudiments of periodic ripples emerge in the initial tens of picoseconds after fs laser irradiation, and the ripple positions keep unmoved until the formation processes complete mainly in a temporal span of 1500 ps. The results suggest that the periodic deposition of laser energy during the interaction between femtosecond laser pulses and sample surface plays a dominant role in the formation of periodic ripples.

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.