Multispectral pansharpening with radiative transfer-based detail-injection modeling for preserving changes in vegetation cover

Whenever vegetated areas are monitored over time, phenological changes in land cover should be decoupled from changes in acquisition conditions, like atmospheric components, Sun and satellite heights and imaging instrument. This especially holds when the multispectral (MS) bands are sharpened for spatial resolution enhancement by means of a panchromatic (Pan) image of higher resolution, a process referred to as pansharpening. In this paper, we provide evidence that pansharpening of visible/near-infrared (VNIR) bands takes advantage of a correction of the path radiance term introduced by the atmosphere, during the fusion process. This holds whenever the fusion mechanism emulates the radiative transfer model ruling the acquisition of the Earth's surface from space, that is for methods exploiting a multiplicative, or contrast-based, injection model of spatial details extracted from the panchromatic (Pan) image into the interpolated multispectral (MS) bands. The path radiance should be estimated and subtracted from each band before the product by Pan is accomplished. Both empirical and model-based estimation techniques of MS path radiances are compared within the framework of optimized algorithms. Simulations carried out on two GeoEye-1 observations of the same agricultural landscape on different dates highlight that the de-hazing of MS before fusion is beneficial to an accurate detection of seasonal changes in the scene, as measured by the normalized differential vegetation index (NDVI)

Super Resolution of Wavelet-Encoded Images and Videos

In this dissertation, we address the multiframe super resolution reconstruction problem for wavelet-encoded images and videos. The goal of multiframe super resolution is to obtain one or more high resolution images by fusing a sequence of degraded or aliased low resolution images of the same scene. Since the low resolution images may be unaligned, a registration step is required before super resolution reconstruction. Therefore, we first explore in-band (i.e. in the wavelet-domain) image registration; then, investigate super resolution. Our motivation for analyzing the image registration and super resolution problems in the wavelet domain is the growing trend in wavelet-encoded imaging, and wavelet-encoding for image/video compression. Due to drawbacks of widely used discrete cosine transform in image and video compression, a considerable amount of literature is devoted to wavelet-based methods. However, since wavelets are shift-variant, existing methods cannot utilize wavelet subbands efficiently. In order to overcome this drawback, we establish and explore the direct relationship between the subbands under a translational shift, for image registration and super resolution. We then employ our devised in-band methodology, in a motion compensated video compression framework, to demonstrate the effective usage of wavelet subbands. Super resolution can also be used as a post-processing step in video compression in order to decrease the size of the video files to be compressed, with downsampling added as a pre-processing step. Therefore, we present a video compression scheme that utilizes super resolution to reconstruct the high frequency information lost during downsampling. In addition, super resolution is a crucial post-processing step for satellite imagery, due to the fact that it is hard to update imaging devices after a satellite is launched. Thus, we also demonstrate the usage of our devised methods in enhancing resolution of pansharpened multispectral images

Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data Fusion Contest

International audienceIn January 2006, the Data Fusion Committee of the IEEE Geoscience and Remote Sensing Society launched a public contest for pansharpening algorithms, which aimed to identify the ones that perform best. Seven research groups worldwide participated in the contest, testing eight algorithms following different philosophies [component substitution, multiresolution analysis (MRA), detail injection, etc.]. Several complete data sets from two different sensors, namely, QuickBird and simulated Pléiades, were delivered to all participants. The fusion results were collected and evaluated, both visually and objectively. Quantitative results of pansharpening were possible owing to the availability of reference originals obtained either by simulating the data collected from the satellite sensor by means of higher resolution data from an airborne platform, in the case of the Pléiades data, or by first degrading all the available data to a coarser resolution and saving the original as the reference, in the case of the QuickBird data. The evaluation results were presented during the special session on Data Fusion at the 2006 International Geoscience and Remote Sensing Symposium in Denver, and these are discussed in further detail in this paper. Two algorithms outperform all the others, the visual analysis being confirmed by the quantitative evaluation. These two methods share the same philosophy: they basically rely on MRA and employ adaptive models for the injection of high-pass details

Image Fusion in Remote Sensing and Quality Evaluation of Fused Images

In remote sensing, acquired optical images of high spectral resolution have usually a lower spatial resolution than images of lower spectral resolution. This is due to physical, cost and complexity constraints. To make the most of the available imagery, many image fusion techniques have been developed to address this problem. Image fusion is an ill-posed inverse problem where an image of low spatial resolution and high spectral resolution is enhanced in spatial-resolution by using an auxiliary image of high spatial resolution and low spectral resolution. It is assumed that both images display the same scene and are properly co-registered. Thus, the problem is essentially to transfer details from the higher spatial resolution auxiliary image to the upscaled lower resolution image in a manner that minimizes the spatial and spectral distortion of the fused image. The most common image fusion problem is pansharpening, where a multispectral (MS) image is enhanced using wide-band panchromatic (PAN) image. A similar problem is the enhancement of a hyperspectral (HS) image by either a PAN image or an MS image. As there is no reference image available, the reliable quantitative evaluation of the quality of the fused image is a difficult problem. This thesis addresses the image fusion problem in three different ways and also addresses the problem of quantitative quality evaluation.Í fjarkönnun hafa myndir með háa rófsupplausn lægri rúmupplausn en myndir með lægri rófsupplausn vegna eðlisfræðilegra og kostnaðarlegra takmarkana. Til að auka upplýsingamagn slíkra mynda hafa verið þróaðar fjölmargar sambræðsluaðferðir á síðustu tveimur áratugum. Myndsambræðsla er illa framsett andhverft vandmál (e. inverse problem) þar sem rúmupplausn myndar af hárri rófsupplausn er aukin með því að nota upplýsingar frá mynd af hárri rúmupplausn og lægri rófsupplausn. Það er gert ráð fyrir að báðar myndir sýni nákvæmlega sama landsvæði. Þannig er vandamálið í eðli sínu að flytja fíngerða eiginleika myndar af hærri rúmupplausn yfir á mynd af lægri rúmupplausn sem hefur verið brúuð upp í stærð hinnar myndarinnar, án þess að skerða gæði rófsupplýsinga upphaflegu myndarinnar. Algengasta myndbræðsluvandamálið í fjarkönnun er svokölluð panskerpun (e. pansharpening) þar sem fjölrásamynd (e. multispectral image) er endurbætt í rúmi með svokallaðri víðbandsmynd (e. panchromatic image) sem hefur aðeins eina rás af hárri upplausn. Annað svipað vandamál er sambræðsla háfjölrásamyndar (e. hyperspectral image) og annaðhvort fjölrásamyndar eða víðbandsmyndar. Þar sem myndsambræðsla er andhverft vandmál er engin háupplausnar samanburðarmynd tiltæk, sem gerir mat á gæðum sambræddu myndarinnar að erfiðu vandamáli. Í þessari ritgerð eru kynntar þrjár aðferðir sem taka á myndsambræðlsu og einnig er fjallað um mat á gæðum sambræddra mynda, þá sérstaklega panskerptra mynda

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

High resolution satellite images for archeological applications: the Karima case study (Nubia region, Sudan)

This work presents an approach based on satellite remotely sensed images and Geomatics techniques aimed at supporting the Italian archeological missions that at the moment are active in the Karima Area (Sudan). It's well known that archaeologists often suffer from lack of updated maps useful to geographically manage the observations coming from the field and, possibly, to address or suggest where digging for new excavations. Specifically for this experience QuickBird and ASTER data were acquired and processed to generate a high scale multispectral orthoimage of the area. The spectral properties of the QB orthoimage were exploited with the purpose of obtaining suggestions about the possible existence of stil lhidden archaeological features