Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy, or CHRIS/Proba,
represents a new generation of satellite images that provide different acquisitions of the same scene at five different
angles. Given the hyperspectral-oriented waveband configuration of the CHRIS images, the scope of its application
would be much wider if the present 17m nadir resolution could be refined. This paper presents the results of three
superresolution methods applied to multiangular CHRIS/Proba data. The CHRIS images were preprocessed and then
calibrated into reflectance using the method described in [1][2]. Automatic registration using an intensity variation
approach described in [3] was implemented for motion estimation. Three methods, namely non-uniform interpolation
and de-convolution [4], iterative back-projection [5], and total variation [6] are examined. Quantitative measures
including peak signal to noise ratio [7], structural similarity [8], and edge stability [9], are used for the evaluation of the
image quality. To further examine the benefit of multi-frame superresolution methods, a single-frame superresolution
method of bicubic resampling was also applied. Our results show that a high resolution image derived from
superresolution methods enhance spatial resolution and provides substantially more image details. The spectral profiles
of selected land covers before and after the application of superresolution show negligible differences, hinting the use of
superresolution algorithm would not degrade the capability of the data set for classification. Among the three methods,
total variation gives the best performance in all quantitative measures. Visual inspections find good results with total
variation and iterative back-projection approaches. The use of superresolution algorithms, however, is complex as there
are many parameters. In this paper, most of the parameter settings were tuned manually or decided empirically.
This paper presents semantic risk estimation of suspected minefields using spatial relationships of minefield indicators extracted from multi-level remote sensing. Both satellite image and pyramidal airborne acquisitions from 900m to 30m flying heights with resolutions from 1m to 2cm resolutions are used for identification of minefield indicators. R-Histogram [1] is a quantitative representation of spatial relationship between two objects in an image. Eight spatial relationships can be generated: 1) LEFT OF, 2) RIGHT OF, 3) ABOVE, 4) BELOW, 5) NEAR, 6) FAR, 7) INSIDE, 8) OUTSIDE. R-Histogram semantics are first generated from selected indicators and metrics such as topological proximity and directional relationships are trained for soft classification of risk index (normalized as 0-1). We presented a framework of how semantic metadata generated from remote sensing images are used in risk estimation. The resultant risk index identified seven out of twelve mine accidents occurred at high risk region. More importantly, comparison with ground truth obtained after mine clearance show that three out of the four identified pattern minefields falls into the area estimated at very high risk. A parcel-based per-field risk estimation can also be easily generated to show the usefulness of the risk index.
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