Noncoherent signal model is widely applied to angular superresolution in forward-looking imaging radar. However, relative phase influences the resolvability of the model in coherent applications. We propose a coherent signal model by replacing the antenna power pattern with the antenna radiation pattern. Experiment results have demonstrated significant resolution improvements for forward-looking imaging in coherent scanning radars.

Gaofen-4 (GF-4) is the first high-resolution geostationary satellite of China, launched on December 29, 2015. Its visible-near infrared optical sensor is capable of imaging the earth at 50 m resolution, covering a 512 km × 512 km area with a minimum imaging interval of 20 s. More than 300 GF-4 images, taken in four sequences with imaging rates of 36 and 69 s per scene, captured the development of Typhoon Megi-2016-from its peak in the afternoon of September 26, 2016 to its dispersion two days later on September 28. These consecutive images recorded the motion field of the typhoon's top clouds nearly continuously. By using advanced image matching technology, the motion has been estimated for every pixel, at subpixel accuracy from image pairs with 179 and 206 s intervals. The process has generated time series of atmospheric motion vector (AMV) fields at 50 m spatial resolution for the whole imaged area. It is the first time, to our knowledge, that such high-resolution and nearly continuous AMV data of a typhoon system have been produced. The data provide accurate measurements of the typhoon top cloud motion speed and direction, and reveal quantitative details of the motion field spatiotemporal evolution at high altitude. The finding that the high altitude top cloud motion speed is significantly lower than that recorded at low altitude below the planetary boundary layer is of scientific value and further exploitation of the motion data can lead to a better understanding of typhoon dynamics and thus improve cyclone modeling.

Multitemporal airborne and satellite imagery data with frequent repeat coverage provide great capability for change detection (CD). When comparing two images taken at different times of day or in different seasons for CD, the variation of topographic shades and shadows caused by the change of sunlight angle can be so significant that it overwhelms the real object and environmental changes, making automatic detection unreliable. An effective CD algorithm, therefore, has to be robust to the illumination variation. In this paper, the robustness of phase correlation (PC) to shadow effects is proven via mathematical analysis, and then, an illumination-invariant change detection (IICD) metric is proposed based on pixel-wise dense PC matching. In the proposed IICD method, a graph-based visual saliency map is introduced for the initial CD followed by an active contour-based segmentation to precisely quantize the change region. Compared to the state-of-the-art CD algorithms, experiments using daily images of a landscape model and Landsat satellite images demonstrate that only the proposed method can effectively detect and precisely segment appearance changes under daily and seasonal sunlight changes.

Illumination variation is one of the major problems in multitemporal earth observation (EO) image matching. Despite much research has been focused on illumination invariant image matching, subpixel image matching under large illumination variation without prior knowledge is still a challenge. This paper proposes a phase correlation decomposition (PCD) theory model in order to analyze the joint effects of zenith and azimuth angles of the lighting source. A novel stepwise least-squares fitting-based PC (SLSF-PC) is proposed to accurately calculate the subpixel image shift by a stepwise function in the frequency domain. Our mathematical investigation is validated by image alignment and stereo dense matching experiments using simulated terrain shading images representing four different landscapes and a multi-illumination remotely sensed image data set containing eight different scenes under seasonal and daily illumination variation. Image matching experiments demonstrate the superior performance of the proposed SLSF-PC compared to the state-of-the-art image matching algorithms, such as speeded up robust features (SURF), mutual information (MI), and normalized cross correlation (NCC). Even under great illumination angle change, the proposed SLSF-PC is able to achieve 0.1 subpixel matching accuracy on average while other methods fail to find the correspondence.

This article presents a novel technique for automatically locating tropical cyclone (TC) centers based on top cloud motions in consecutive geostationary satellite images. The high imaging rate and spatial resolution images of the Gaofen-4 geostationary satellite enable us to derive pixel-wise top cloud motion data of TCs, and from the data, TC spiral centers can be accurately determined based on an entirely different principle from those based on static image features. First, a physical motion field decomposition is proposed to eliminate scene shift and TC migration in the motion data without requiring any auxiliary geolocation data. This decomposition does not generate the artifacts that appear in the results of the previously published motion field decomposition. Then, an algorithm of a motion direction-based index embedded in a pyramid searching structure is fully designed to automatically and effectively locate the TC centers. The test shows that the TC concentric motions are more clearly revealed after the proposed motion field decomposition and the located centers are in good agreement with the cloud pattern centers in a visual sense and also with the best track data sets of four meteorological agencies.

Traditional DEM (Digital Elevation Model) generation from satellite imagery is based on stereo pair or triple images using wide baseline. These days, a number of low-cost microsatellites, such as SkySat, have been launched. The high resolution video image sequences they provided result in large number of image frames and more flexible selection of baseline, which allows us to design a 3D super resolution scene reconstruction approach based on multiple narrow baseline stereo pairs. The multiple disparity maps, generated using Phase Correlation (PC) based sub-pixel stereo matching algorithm, are co-registered pixel by pixel and up-sampled and then stacked to produce a super resolution scene depth map. The scene depth image constructed in this way has three advantages: i) “pixel locking” error typical for sub-pixel image matching is minimized; ii) super resolution is achieved; and iii) occlusion problem is minimized by multiple narrow baseline stereo pairs. A 3D scene super resolution reconstruction example is demonstrated using a SkySat video image of Usak, Western Turkey.

Due to the great computation load and memory cost of the matrix-vector multiplication, the sparse reconstruction algorithms are severely limited in the applications of radar imaging with real data. In order to solve this problem, we construct a backprojection-based range-azimuth decoupled operator (BP-RADOp) and combine the complex approximate message passing algorithm (CAMP) with it. We call this algorithm BP-CAMP in this letter. Since BP-RADOp retains the merits of the backprojection method entirely (i.e., perfect motion compensation for any flight path, precise focus for arbitrarily wide bandwidths and integration angles, low artifact levels, unlimited scene size, and strictly local processing), it has universal applicability in comparison with the other decoupled operators deduced from the fast Fourier transform-based image formation algorithms. The theoretical analysis indicates when BP-CAMP and CAMP are both used to reconstruct large-scale observed scenes; the former has lower computation load and memory cost than the latter. Meanwhile, it is demonstrated that BP-CAMP achieves high-quality synthetic aperture radar imaging with undersampled echo data, and it is as robust as CAMP to additive noise by the simulations and real data processing.

In this paper, we present a Phase Correlation (PC) based change detection algorithm which is able to automatically detect very small target such as the Mars rover in multi-temporal HiRISE images under illumination variation and camera motion. The robustness of PC to illumination variation was proved by our previous work while the geometric distortion caused by camera motion can be eliminated by PC-based pixel-wise image co-registration. Experiments using three HiRISE images taken under different illumination and view angle demonstrate that our algorithm can robustly detect the Mars rover as small as 6×9 pixels in conditions of varying illumination and camera 3D status.

Phase correlation (PC) is a well-established image matching algorithm. It is robust to the variation of image contrast and brightness, but whether it is invariant to local illumination change, particularly sun angle change, is yet to be investigated. This paper presents our study to prove and demonstrate the robustness of illumination invariance of the PC algorithm via mathematical analysis and image matching experiments. First, a 3-D space named slope-aspect-intensity (SAI) is proposed to characterize the 3-D relationship between image intensity and terrain slope/aspect angles for a given illumination geometry. Based on the SAI space, the mathematical relationship between PC cross-power spectra and illumination direction (e.g., solar azimuth and zenith angles) is analyzed. The impact of illumination angle variation between two images for matching is then rigorously investigated via experiments using simulated terrain shading images from a digital elevation model and real optical images taken under different sun illumination conditions. Our study confirms that PC is robustly invariant to illumination and therefore can achieve reliable matching between images taken under different solar illumination conditions for various remote sensing applications.

We introduce a total variation (TV) regularization model for synthetic aperture radar (SAR) image despeckling. A dual-formulation-based adaptive TV (ATV) regularization method is applied to solve the TV regularization. The parameter adaptation of the TV regularization is performed based on the noise level estimated via wavelets. The TV-regularization-based image restoration model has a good performance in preserving image sharpness and edges while removing noises, and it is therefore effective for edge preserve SAR image despeckling. Experiments have been carried out using optical images contaminated with artificial speckles first and then SAR images. A despeckling evaluation index (DEI) is designed to assess the effectiveness of edge preserve despeckling on SAR images, which is based on the ratio of the standard deviations of two neighborhood areas of different sizes of a pixel. Experimental results show that the proposed ATV method can effectively suppress SAR image speckles without compromising the edge sharpness of image features according to both subjective visual assessment of image quality and objective evaluation using DEI.

In this paper, we introduce a total variation (TV) regularization model for SAR image despeckling. A dual formulation based adaptive total variation (ATV) regularization method is applied to solve the TV regularization. The parameter adaptation of the TV regularization is performed based on the noise level estimated via wavelets. The TV regularization based image restoration model has a good performance in preserving image sharpness and edges while removing noises and it is therefore effective for edge preserve SAR image despeckling. Experiments have been carried out using optical images contaminated with artificial speckles first and then SAR images. An evaluation index is designed to assess the effectiveness of edge preserve despeckling on SAR images, which is based on the ratio of the standard deviations of two neighborhood areas of a pixel with different sizes. Experimental results show that the proposed method can effectively suppress SAR image speckles without compromise the edge sharpness of image features according to both subjective visual examination and objective evaluation indices of image quality.

Differential Interferometric Synthetic Aperture Radar (DInSAR) is an effective technique to measures the surface displacement caused by strong earthquakes, such as the 2008 Wenchuan earthquake in China. However, in the area subject to the most significant deformation along the Longmenshan fault zone, the coherence between pre- and after- earthquake SAR images is completely lost because of the earthquake induced violent and chaotic destruction on the land surface and consequently, no surface displacement data can be measured. The missing data were recovered using Adaptive Local Kriging (ALK) method to produce a complete Line Of Sight (LOS) displacement map. As a further step to characterize the 3D (three-dimensional) co-seismic deformation, horizontal displacement maps were generated using the Phase Correlation based Image Analysis System (PCIAS), and in combination with the ALK DInSAR data, the vertical displacement map can then be decomposed. Thus, a 3D displacement dataset is accomplished. This dataset shows the thrust and right-lateral strike slip motions along the Longmenshan fault system with two major uplift in Yingxiu and Beichuan areas respectively.

This paper presents an efficient Phase Correlation based Image Analysis System (PCIAS) for high quality DEM generation. A multi-resolution phase correlation based disparity estimation and refinement algorithm has been implemented in PCIAS. It can easily cope with the precise disparity estimation from sub-pixel to very large disparity range with varying baseline/distance ratio in vertical or slightly oblique view stereo imaging. The PCIAS is now a fully operational, professional C++ software package equipped with a robust phase correlation engine, which is among the most advanced technology for sub-pixel image feature shift analysis, and is able to achievethpixel accuracy in dense disparity map estimation. Our experiment indicates PCIAS can generate high quality DEM from very narrow baseline satellite image pairs with view angle difference as small as just 1 degree.

Kriging technique is widely used for raster data interpolation, such as digital elevation models and precipitation maps. It depends on the spatial distribution of sampled values and the variogram model which fit an empirical global semivariogram of entire image. However, such a variogram model may not suitable for datasets with strong trends of the spatial distribution, such as differential interferometric synthetic aperture radar (DInSAR) data. Here we propose a modified kriging method, adaptive local kriging (ALK), for retrieve of the missing data in the decoherence zone of the ALOS PALSAR DInSAR data of 2008 Wenchuan earthquake. In the ALK, dynamic linear variogram models fitting the local empirical semivariograms are used instead of a global variogram model for the whole image. The localized adaptive approach avoids drastic errors in the large decoherence gaps and the value prediction is thus significant improved as confirmed in comparison with the original DInSAR data.

The information enhancement of an hyperspectral image through building shadows removal is presented. Starting from the energy conservation model and considering the development of the simulated reflectance algorithm, the study demonstrates how it is possible to exploit the wide spectrum of an hyperspectral image in urban areas allowing the visualization of image details which were shaded by urban structures. Moreover, the described methodology positively affects land classification: the accuracy detection of four land cover classes (vegetation, buildings, asphalt and bare soil) has been improved using as input to a neural network classifier simulated reflectance image instead of the original one. The experimental data consisted of an Airborne Hyperspectral Scanner (AHS) image acquired over the city of Madrid.

This paper presents an enhanced robust phase correlation (ERPC) method for sub-pixel disparity estimation together with a package of refinement techniques for DEM generation. The multi-scale ERPC algorithm can cope with very large disparity measurement on the one hand and improve sub-pixel accuracy by entirely avoiding the ill-posed problem of 2D phase unwrapping on the other. Recognizing the inherent weakness of phase correlation as an area matching based method, we then present a refinement scheme with compound phase correlation (CPC) and Median Shift Propagation (MSP) filter techniques to deal with the unreliable disparity estimates around the depth discontinuities, featureless areas and other low correlation areas. Through this refinement scheme, we are able to greatly improve the accuracy of phase correlation based disparity estimation for DEM generation from stereo image pairs with versatile baseline settings (from narrow to wide baseline).

To obtain depth-from-stereo imagery, it is traditionally required that the baseline separation between images (or the base-to-height ratio) be very large in order to ensure the largest image disparity range for effective measurement. Typically, aB/Hratio in the range of 0.6-1 is preferred. As a consequence, most existing stereo-matching algorithms are designed to measure disparities reliably with only integer-pixel precision. However, wide baselines may increase the possibility of occlusion occurring between highly contrasting relief, imposing a serious problem to digital elevation model (DEM) generation in urban and highly dissected mountainous areas. A narrow-baseline stereo configuration can alleviate the problem significantly but requires very precise measurements of disparity at subpixel levels. In this paper, we demonstrate a stereo-matching algorithm, based upon the robustphase correlationmethod, that is capable of directly measuring disparities up to 1/50th pixel accuracy and precision. The algorithm enables complete and dense surface shape information to be retrieved from images with unconventionally lowB/Hratios (e.g., less than 0.01), potentially allowing DEM generation from images that would otherwise not be deemed suitable for the purpose.

This paper presents a robust phase correlation technique and a compound phase correlation method for reliable sub-pixel image feature matching and motion estimation. A phase fringe filter and a highly robust estimator QMDPE are used to improve the fitting accuracy of the phase difference plane in Fourier domain. A compound phase correlation method is proposed to identify and decompose the multiple motion patterns in phase difference matrix at sub-pixel accuracy. This compound phase correlation method is the breakthrough in improving the robustness of phase correlation based feature matching around motion boundary or depth discontinuity. With the robust phase correlation and the compound phase correlation combined algorithm, the optical flow estimation and stereo matching achieved remarkable accuracy, especially around the areas with the motion boundary or depth discontinuity. 1

We demonstrate an automated stereo-matching algorithm capable of extracting disparity/depth information from stereo image pairs with an unconventionally narrow baseline separation i.e. low base-to-height ratio (B/H), thus potentially allowing digital elevation models (DEMs) to be derived from images that previously might not have been considered suitable for stereo applications. For very small B/H ratios the disparity magnitudes may be reduced to sub-pixel levels that conventional stereo-matching algorithms fail to measure. By utilising a new sub-pixel image matching algorithm, based upon the phase correlation (PC) method, we are able to measure the very subtle, sub-pixel, disparities that result from image pairs with B/H ratios as small as 0.06. Initial tests with this algorithm on SPOT 5 satellite image data have demonstrated that this routine is capable of generating very dense and detailed disparity/depth maps of a scene for DEM generation without prior knowledge of the satellite/sensor parameters, whilst also being exceptionally sensitive to small scale textural features, robust to noise, and efficient to implement.

In this paper, a novel Asymmetrical Corner Detector (ACD) algorithm based on the auto-correlation is presented. Based on the study of the fact that asymmetrical corner points are the most common reality in remotely sensed imagery data, the ACD is designed to detect interest points more favourable to slightly asymmetrical points rather than ideal symmetrical points. The experimentation results using images taken by different sensors indicate that the ACD has obtained excellent performance in terms of point localization and computational efficiency. It is more capable of selecting high quality ground control points (GCPs) than some well-established corner detectors, e.g. the Harris Corner Detector [1]. A semi-automatic image co-registration scheme is then proposed, which employed the ACD algorithm to extract evenly distributed GCPs across the overlapped area in the reference image.

We have developed and used a grid-based geoinformatics infrastructure and analytical methods for investigating the relationship between macro and microscale earthquake deformational processes by linking geographically distributed and computationally intensive earthquake monitoring and modelling tools. Using this infrastructure, measurement of lateral co-seismic deformation is carried out with imageodesy algorithms running on servers at the London eScience Centre. The resultant deformation field is used to initialise geomechanical simulations of the earthquake deformation running on supercomputers based at the University of Oklahoma. This paper describes the details of our work, summarizes our scientific results and details our experiences from implementing and testing the distributed infrastructure and analysis workflow.

The images of imageodesy derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image pairs across an earthquake event exhibit severe horizontal and vertical striping noise patterns that obscure the desired information relating to earthquake-induced terrain displacements. The periodic noise patterns were analyzed to reveal that the principal cause of these errors was due to the two-way across-track scanning mechanism of the ETM+ instrument in relation to the minor shift of the satellite orbits between two acquisitions. We first focused on the design of selective filters pinpointing the noise frequencies of horizontal striping based on frequency-domain analysis, via fast Fourier transform (FFT). As a result, the horizontal striping patterns have been successfully eliminated, but meanwhile the systematic varying frequency nature of the more complicated wavy vertical striping patterns is better revealed. This has led to the successful design of an adaptive FFT frequency filtering mechanism based on the function characterizing the relationship between angular speed of a scanner imaging system and its corresponding line speed on the curved surface of the Earth. Finally, the horizontal and vertical striping patterns have been successfully eliminated by semiautomatic selective and adaptive filtering procedures without subduing the key information of coseismic displacement

We present the development and use of a novel distributed geohazard modeling environment for the analysis and interpretation of large scale earthquake data sets. Our work demonstrates, for the first time, how earthquake-related surface deformation measured from satellite images using imageodesy algorithms is coupled with analysis and simulation using finite-element numerical models. Our work realises a real time distributed analytical environment where analysis and simulation are closely coupled; integrating high performance implementations of image mining components executing on dedicated Discovery Net servers at Imperial College London, UK and high performance implementations of finiteelement models executing at specialised servers at the University of Oklahoma, USA. Novel scientific results produced using our data sets provide a valuable insight into earthquake analysis. In addition, our informatics work provides a novel high performance computing framework and methods for the application of complex knowledge discovery methods to understanding earthquake dynamics. Furthermore, the realisation of our distributed computing platform is based on the implementation of a set of open standards, making its results accessible over the Grid to the wider scientific community.

The Three Gorges Dam Project (TGP) has raised international attention because of its great potential for hydro-electrical power generation and flood control, as well as for its potential hazardous effects on the environment and ecology, and for the migration and resettlement of local inhabitants. Slope instability is one common problem in the Three Gorges area and this paper reports a regional assessment of landslide hazard based on ASTER imagery and Digital Elevation Model (DEM), in combination with limited field investigation. A simple model based on the geometric mean and Boolean decision rules has been applied to a multi-criterion dataset to generate a map of landslide hazard.

Topographic decorrelation due to the local surface slope has been an obstacle to interferometric synthetic aperture radar (InSAR) applications. A modified spatial decorrelation function is derived as a function of the baseline and topography. This function explains the origin of the total topographic decorrelation phenomenon on the slopes directly facing radar illumination and layover, which may mislead InSAR coherence image interpretation. The authors define critical terrain slope (or critical incidence angle) as the angle for which two SAR signals completely decorrelate regardless of surface stability. It is found that the width of the critical terrain slope increases with the increase of the component of the baseline perpendicular to the radar look direction. A new analytical method, the ratio coherence imagery, is then introduced to highlight total topographic decorrelation against the temporal decorrelation features. The applications of this methodology are demonstrated in selected locations in the Sahara Desert, Algeria, and Almer/spl inodot//spl acute/a, Spain, using ERS-1 and ERS-2 SAR data.