Terrain classification in urban wetlands with high-spatial-resolution multispectral imagery

The pan-sharpening is a widely used operation in remote sensing image processing, this operation aims at combining an observable high spatial resolution panchromatic image with a multispectral one, to generate an unobservable image with the high spatial resolution of the former and a high spectral resolution of the latter. Generally, papers dealing with this problem omit the geometric part and suppose that these images are perfectly aligned, which is not necessarily the case for the raw imagery, where even the different bands in the multispectral imagery are misaligned. In this paper, new method for multispectral and panchromatic image registration is proposed to deal with the misalignment problem that reduces the pansharpening quality. This method called Dense Vector Matching (DVM) is based on the matching of a whole line-vector or column-vector from a reference band with the corresponding vector in a target band. DVM is applied on real data and has given acceptable results, where the QNR index of the pan-sharpening is better for images after band registration, also the registration error is reduced to sub-pixel using the proposed approach.

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A Novel Classification Optimization Approach Integrating Class Adaptive MRF and Fuzzy Local Information for High Spatial Resolution Multispectral Imagery

Applied Sciences ◽

10.3390/app8101792 ◽

2018 ◽

Vol 8 (10) ◽

pp. 1792 ◽

Cited By ~ 1

Author(s):

Yuejin Zhou ◽

Hua Zhang ◽

Xiaoding Xu ◽

Mingpeng Li ◽

Lihui Zheng ◽

...

Keyword(s):

Spatial Resolution ◽

Energy Function ◽

High Spatial Resolution ◽

Local Information ◽

Data Sets ◽

Optimization Approach ◽

Spatial Dependency ◽

Multispectral Imagery ◽

Iterated Conditional Modes ◽

Class Labels

This paper develops a novel classification optimization approach integrating class adaptive Markov Random Field (MRF) and fuzzy local information (CAMRF-FLI) for high spatial resolution multispectral imagery (HSRMI). Firstly, the raw classification results, including initial fuzzy memberships and class labels of every pixel, are achieved by a pixel-wise classification method for a given image. Secondly, the class adaptive MRF-based data energy function is developed to integrate class spatial dependency information. Thirdly, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw classification map is regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of CAMRF-FLI is performed by two data sets. The results indicate it can refine the classification map in homogeneous areas, meanwhile, reduce most of the edge blurring artifact, and improve the classification accuracy compared with some conventional approaches.

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High spatial resolution AEM observations of yttrium segregation in chromia scales grown on Co-45%Cr

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100144103 ◽

1986 ◽

Vol 44 ◽

pp. 518-519

Author(s):

K. Przybylski ◽

A. J. Garratt-Reed ◽

G. J. Yurek

Keyword(s):

Spatial Resolution ◽

Outer Surface ◽

Maximum Concentration ◽

High Spatial Resolution ◽

Reactive Elements ◽

Chromia Scales

The addition of so-called “reactive” elements such as yttrium to alloys is known to enhance the protective nature of Cr2O3 or Al2O3 scales. However, the mechanism by which this enhancement is achieved remains unclear. An A.E.M. study has been performed of scales grown at 1000°C for 25 hr. in pure O2 on Co-45%Cr implanted at 70 keV with 2x1016 atoms/cm2 of yttrium. In the unoxidized alloys it was calculated that the maximum concentration of Y was 13.9 wt% at a depth of about 17 nm. SIMS results showed that in the scale the yttrium remained near the outer surface.

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