Adaptive Mollifiers for High Resolution Recovery of Piecewise Smooth Data from its Spectral Information

A fusion approach is proposed to refine the resolution of multi-spectral images using the corresponding high-resolution panchromatic images. The technique is based on intensity modulation and non-separable wavelet frame. The high-resolution panchromatic image is decomposed by the non-separable wavelet frame. Then the wavelet coefficients are used as the factor of modulating to modulate the multi-spectral image. Experimental results indicate that, comparing with the traditional methods, the proposed method can efficiently preserve the spectral information while improving the spatial resolution of remote sensing images.

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Interpretation of high-resolution optical imagery with evidential fusion of spectral information and object attributes

2006 9th International Conference on Information Fusion ◽

10.1109/icif.2006.301653 ◽

2006 ◽

Author(s):

Y. Allard ◽

A. Jouan

Keyword(s):

High Resolution ◽

Spectral Information ◽

Optical Imagery

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Isotope specific resolution recovery image reconstruction in high resolution PET imaging

Medical Physics ◽

10.1118/1.4870985 ◽

2014 ◽

Vol 41 (5) ◽

pp. 052503 ◽

Cited By ~ 8

Author(s):

Fotis A. Kotasidis ◽

Georgios I. Angelis ◽

Jose Anton-Rodriguez ◽

Julian C. Matthews ◽

Andrew J. Reader ◽

...

Keyword(s):

High Resolution ◽

Image Reconstruction ◽

Pet Imaging ◽

Resolution Recovery

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Animal remains from the Late Medieval kitchen of the Esztergom archdiocese, Hungary - the benefits of screening

Starinar ◽

10.2298/sta2171231g ◽

2021 ◽

pp. 231-251

Author(s):

Erika Gál ◽

László Bartosiewicz

Keyword(s):

High Resolution ◽

Fish Farming ◽

Taxonomic Diversity ◽

Local Production ◽

Resolution Recovery ◽

Documentary Sources ◽

15Th Century ◽

Animal Remains ◽

Animal Bone ◽

Shed Light

Medieval animal remains from the Esztergom archbishopric (Hungary) were screened using 5 mm and 2 mm mesh sizes, aimed at the high-resolution study of fish and bird remains and helping to achieve better comparisons with documentary sources. This is the first medieval assemblage in Hungary recovered using screening. A total of 7,294 animal remains are studied here, representing the 14th and 15th century. The screening resulted in quantities of fish and bird bones. The large find numbers also multiplied the taxonomic diversity. In addition to the remains of new, small-bodied species, bones of young fish showed a diachronic increase in the contribution of carp and young pike to the diet. This seems consonant with the expansion of medieval fish farming. Remains of juvenile birds could also be identified. Some worked bones recovered by screening indicate the manufacturing or reparation of crossbows at the site. Thanks to these details, our material stands out among other contemporaneous animal bone assemblages from the Carpathian Basin. Comparisons between sites, however, must be done with caution, as our data are qualitatively different from others. Large bones of livestock and the near absence of those from large game may be interpreted in the light of other hand-collected samples, while fish and bird remains and even the abundance of brown hare need to be seen in part as a product of high-resolution recovery. The newly discovered spectrum of animal remains could be profitably interpreted in the light of late 15th century accounting books of the archbishop. Although these documentary sources slightly post-date our material, they shed light on the complexities of meat procurement between possibly local production and trade.

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Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning

Remote Sensing ◽

10.3390/rs12091357 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1357 ◽

Cited By ~ 3

Author(s):

Maitiniyazi Maimaitijiang ◽

Vasit Sagan ◽

Paheding Sidike ◽

Ahmad M. Daloye ◽

Hasanjan Erkbol ◽

...

Keyword(s):

Machine Learning ◽

High Resolution ◽

Data Fusion ◽

Unmanned Aerial Vehicle ◽

Canopy Structure ◽

Canopy Height ◽

Spectral Information ◽

Structure Information ◽

Crop Monitoring ◽

Aerial Vehicle

Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security. The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring using satellite/unmanned aerial vehicle (UAV) data fusion and machine learning. Worldview-2/3 satellite data were tasked synchronized with high-resolution RGB image collection using an inexpensive unmanned aerial vehicle (UAV) at a heterogeneous soybean (Glycine max (L.) Merr.) field. Canopy spectral information (i.e., vegetation indices) was extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) was derived from UAV RGB imagery. Canopy spectral and structure information and their combination were used to predict soybean leaf area index (LAI), aboveground biomass (AGB), and leaf nitrogen concentration (N) using partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) with a newly proposed activation function. The results revealed that: (1) UAV imagery-derived high-resolution and detailed canopy structure features, canopy height, and canopy coverage were significant indicators for crop growth monitoring, (2) integration of satellite imagery-based rich canopy spectral information with UAV-derived canopy structural features using machine learning improved soybean AGB, LAI, and leaf N estimation on using satellite or UAV data alone, (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent and led to better model performance, (4) the ELR model with the newly proposed activated function slightly outperformed PLSR, RFR, and SVR in the prediction of AGB and LAI, while RFR provided the best result for N estimation. This study introduced opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring.

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