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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. [ABSTRACT FROM PUBLISHER]

adjacent effect caused by a nonideal system point spread function (PSF) is an important source generating additional errors in radiometric measurements. Traditional recovery methods, i.e., inverse and Wiener filters, are attempted to restore the scene without the influence of RSF, which is inexistent in real observations and usually leads to the ringing artifact as well as uncertainties in noise amplification (NA). In this paper, a novel spatial-response matched filter (SRMF) and its processing method are established, where the aimed scene is supposed to be observed by instruments with higher PSF performance. Also, NA effects triggered by SRMF are precisely modeled and used for the further evaluation of radiometric accuracy (RA) variation after SRMF processing. Simulation results indicate that the SRMF method is more suitable for images with lower noise level and greater variation in the targets' radiation, where RA performance could be improved in a certain extent. Meanwhile, when the imager in a Multifunctional Transport Satellite is selected as the reference one with high-performance PSF, the SRMF for Fengyun-2 satellite is set up and applied for observations in thermal infrared band during April andMay 2013. After SRMF processing, the recovered images show significant improvements in both visual effects and RA, where the increase of RA is expected to be 2-6 K at 190 K in statistics. Such a progress is believed to be beneficial to tropical cyclone intensity estimation as well as for other relevant products, i.e., cloud classification generation. [ABSTRACT FROM AUTHOR]

Ever-expanding networks of surf cameras offer a unique opportunity to monitor the coastline over large expanses at very little cost compared to traditional in situ survey methods. Here, we describe and test a new coastal monitoring system maintained by CoastalCOMS Pty Ltd. at their test site at Gold Coast, Australia. The two-camera system monitors two highly sensitive 4-km stretches of sandy coastline adjacent to high-value assets. The traditional static multi-camera setup has been replaced by a single rotational camera. A 14-month data set, encompassing one major storm, a recovery period, and a seasonal cycle, was analyzed. Positive shoreline detections using the new camera system were available 64% of the time (roughly 145 days of the available 226, where daily offshore significant wave heights Hs ≤ 1 m). Comparison of the CoastalCOMS-derived shorelines and in situ survey data showed a mean shoreward bias of 25.5 m. Daily shoreline estimates were used to calculate weekly and five-week running mean beach widths at both sites. Analysis showed that both sites eroded between 15-22 m during the May 2009 storm and then recovered during the proceeding five-month calm period. Distinct intersite variability was observed between the more exposed Northern Beaches that displayed an annual shoreline cycle and very little intrasite variability and the more sheltered southern Palm Beach site that displayed large intrasite spatial variability and sensitivity to changes to both wave direction and wave height. [ABSTRACT FROM AUTHOR]

Spatial characteristics of thermal infrared (TIR) images are generally described by image-derived point spread function (IDPSF) for sampled imaging system. Most investigators working on IDPSF on-orbit evaluation focus on visible and near-infrared images and pay little attention on TIR ones, the reason of which lies on the fact that the assumption of an observed target with an ideal step profile is not suitable for TIR band. A new approach for TIR images is proposed here based on a more common slope-profile model, where the transition features, i.e., the sample number (Nslope) and the beginning position of slope interval, are extracted from the observed signals. Simulation results show that the estimation performance will be increased, with lower noise level as well as higher system modulation transfer function value at half of normalized spatial frequency (MTF0.5) or smaller Nslope. For a good performance system with noise squared-root variance (SRV) nomore than 1.0 count and MTF0.5 between 0.3 and 0.4, the relative error between simulated and estimated MTF0.5's is less than 20%. The IDPSF of FY-2C satellite TIR band is estimated, and the derived MTF0.5 is properly close to on-ground testing results. The image quality of FY-2C TIR band has been improved by using Wiener filtering and the estimated IDPSF, which eventually benefits the cloud detection product with more detectable low fractus and increased detection accuracy by about 1% in winter. Considering a step-profile target as a special case of this model, the proposed approach is also suitable for visible and near-infrared images. [ABSTRACT FROM AUTHOR]

In this paper, we examine the accuracy of manifold coordinate representations as a reduced representation of a hyperspectral imagery (HSI) lookup table (LUT) for bathymetry retrieval. We also explore on a more limited basis the potential for using these coordinates for modeling other in water properties. Manifold coordinates, are chosen because they are a data-driven intrinsic set of coordinates, which naturally parameterize nonlinearities that are present in HSI of water scenes. The approach is based on the extraction of a reduced dimensionality representation in manifold coordinates of a sufficiently large representative set of HSI. The manifold coordinates are derived from a scalable version of the isometric mapping algorithm. In the present and in our earlier works, these coordinates were used to establish an interpolating LUT for bathymetric retrieval by associating the representative data with ground truth data, in this case from a Light Detection and Ranging (LIDAR) estimate in the representative area. While not the focus of the present paper, the compression of LUTs could also be applied, in principle, to LUTs generated by forward radiative transfer models, and some preliminary work in this regard confirms the potential utility for this application. In this paper, we analyze the approach using data acquired by the Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS) hyperspectral camera over the Indian River Lagoon, Florida, in 2004. Within a few months of the PHILLS overflights, Scanning Hydrographic Operational Airborne LIDAR Survey, LIDAR data were obtained for a portion of this study area, principally covering the beach zone and, in some instances, portions of contiguous river channels. Results demonstrate that significant compression of the LUTs is possible with little loss in retrieval accuracy. [ABSTRACT FROM AUTHOR]

An efficient, physics-based remote bathymetry method for the littoral zone is described and illustrated with applications to QuickBird, Littoral Airborne Sensor: Hyper- spectral (LASH), and Airborne Visible/Infrared Spectrometer (AVIRIS) spectral imagery. The method combines atmospheric correction, water reflectance spectral simulations, and a linear unmixing bathymetry algorithm that accounts for water surface reflections, thin clouds, and variable bottom brightness, and can incorporate blends of bottom materials. Results include depth maps, bottom color visualizations, and in favorable cases, approximate descriptions of the water composition. In addition, atmospheric correction was advanced through new capabilities added to the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and Moderate Resolution Transmittance (MODTRAN) codes, including characterization of the aerosol wavelength dependence and a discrete-ordinate-method radiative transfer scaling technique for rapid calculation of multiply scattered radiance. [ABSTRACT FROM AUTHOR]