I use the quick drying, 2-Part Epoxy (the one in the double injection syringes). I apply it to the surface (which has to be clean), then let it “set-up” for a minute or two, then put the second piece. I found its not as messy if you let the glue cure a bit before assembly. It’s easier to work with when its tacky.

Abundance map estimation — Given the endmember signatures, it is useful to estimate the fractional amount of each endmember present in each pixel. You can generate the abundance maps for each endmember, which represent the distribution of endmember spectra in the image. You can label a pixel as belonging to an endmember spectra by comparing all of the abundance map values obtained for that pixel.

I’ve played with “glass glues” found in the local hardware store and craft stores, some are a variant of super glue — very brittle and not always completly secure, others are thick and goopy kind of like E900 (I think that’s the name) — these tend to be too thick and again not always completely secure. I’ve also used the fake stained glass paint (gallery glass by plaid) as a glue — found a reference to using it as a glue in a magazine once but found that if it’s exposed to water or moisture it doesn’t work.

Detect changes in hyperspectral images over time. For examples of change detection, see Change Detection in Hyperspectral Images and Map Flood Areas Using Sentinel-1 SAR Imagery.

A testimonial from a gentleman that glued the porcelain cap back onto his bridge, indicates it would fit glass to metal needs.

It isn't pollution that's harming the environment. It's the impurities in our air and water that are doing it." George W Bush

Hyperspectral imagedownload

clear siliconw should work for the eye, just clean real well with alcohol first. For glass to glass the uv light and glue is the only thing that will hold up to time. I’ve used it for resotations to vintage glass lamps and windows for about 18 years now and it still holds. Edie

A really good glue for bonding glass & metal is 2-ton epoxy. It is important to give the surface a little ‘tooth’ to get a good bond. If you can etch or sand the the materials where they are to contact, you will get the best results.

Endmember extraction — The spectra of the endmembers are prominent features in the hyperspectral data and can be used for efficient spectral unmixing of hyperspectral images. Convex geometry based approaches, such as pixel purity index (PPI), fast iterative pixel purity index (FIPPI), and N-finder (N-FINDR) are some of the efficient approaches for endmember extraction.

Use the fippi function to estimate the endmembers by using the FIPPI approach. The FIPPI approach is an iterative approach, which uses an automatic target generation process to estimate the initial set of unit vectors for orthogonal projection. The algorithm converges faster than the PPI approach and identifies endmembers that are distinct from one another.

To visualize and understand the object being imaged, it is useful to represent the data cube as a 2-D image by using color schemes. The color representation of the data cube enables you to visually inspect the data and supports decision making. You can use the colorize function to compute the Red-Green-Blue (RGB), false-color, and color-infrared (CIR) representations of the data cube.

Use the ppi function to estimate the endmembers by using the PPI approach. The PPI approach projects the pixel spectra to an orthogonal space and identifies extrema pixels in the projected space as endmembers. This is a non-iterative approach, and the results depend on the random unit vectors generated for orthogonal projection. To improve results, you must increase the random unit vectors for projection, which can be computationally expensive.

You can also use spectral matching to identify materials or perform target detection, detecting specific targets in a hyperspectral image when the spectral signature of the target is distinct from other regions in the hyperspectral image. However, when the spectral contrast between the target and other regions is low, spectral matching becomes more challenging. In such cases, you must use more sophisticated target detection algorithms, such ad those provided by the detectTarget function, that consider the entire hyperspectral data cube and use statistical or machine learning methods. For more information on spectral matching and target detection techniques, see Spectral Matching and Target Detection Techniques.

The hyperspectral imaging sensors typically have high spectral resolution and low spatial resolution. The spatial and the spectral characteristics of the acquired hyperspectral data are characterized by its pixels. Each pixel is a vector of values that specify the intensities at a location (x,y) in z different bands. The vector is known as the pixel spectrum, and it defines the spectral signature of the pixel located at (x,y). The pixel spectra are important features in hyperspectral data analysis. But these pixel spectra gets distorted due to factors such as sensor noise, low resolution, atmospheric effects, and spectral distortions from sensors.

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The hyperpca and hypermnf functions reduce the spectral dimensionality of the data cube by using the PCA and MNF transforms respectively. You can use the pixel spectra derived from the reduced data cube for hyperspectral data analysis.

Hyperspectral imageprocessing

Interpret the pixel spectra by performing spectral matching using the spectralMatch function or target detection using the detectTarget function. Spectral matching identifies the class of an endmember material by comparing its spectra with one or more reference spectra. The reference data consists of pure spectral signatures of materials, which are available as spectral libraries. Use the readEcostressSig function to read the reference spectra files from the ECOSTRESS spectral library. Then, you can compute the similarity between the spectra in the ECOSTRESS library files and the spectra of an endmember material by using the spectralMatch function.

Hyperspectral imageclassification

To enhance the spatial resolution of a hyperspectral data, you can use image fusion methods. The fusion approach combines information from the low resolution hyperspectral data with a high resolution multispectral data or panchromatic image of the same scene. This approach is also known as sharpening or pansharpening in hyperspectral image analysis. Pansharpening specifically refers to fusion between hyperspectral and panchromatic data. You can use the sharpencnmf function for sharpening hyperspectral data using coupled non-matrix factorization method.

It seems I got this link from another thread on WC. It is a link that you can put what you want to glue together and you will get a recomendation on what to use. . . . I love the internet!

I made a stained glass mobile of fish, the eyes on them are either small glass nuggets or some small end of mandrel beads that I made. I’ve had to re-glue them a dozen times! The mobile will just be hanging in my booth at a show and plop! There goes an eye! Very annoying! Because it’s a black glass eye on an opaque orange glass fish I know the UV glue isn’t going to work.

Hyperspectral imageexample

The other preprocessing step that is important in all hyperspectral imaging applications is dimensionality reduction. The large number of bands in the hyperspectral data increases the computational complexity of processing the data cube. The contiguous nature of the band images results in redundant information across bands. Neighboring bands in a hyperspectral image have high correlation, which results in spectral redundancy. You can remove the redundant bands by decorrelating the band images. Popular approaches for reducing the spectral dimensionality of a data cube include band selection and orthogonal transforms.

A spectral index is a function such as a ratio or difference of two or more spectral bands. Spectral indices delineate and identify different regions in an image based on their spectral properties. By calculating a spectral index for each pixel, you can transform the hyperspectral data into a single-band image where the index values indicate the presence and concentration of a feature of interest. Use the spectralIndices and customSpectralIndex functions to identify different regions in the hyperspectral image. For more information on spectral indices, see Spectral Indices.

Non-destructive testing or visual inspection applications such as maturity monitoring of fruits.

PCA transforms the data to a lower dimensional space and finds principal component vectors with their directions along the maximum variances of the input bands. The principal components are in descending order of the amount of total variance explained.

Hyperspectral image processing involves representing, analyzing, and interpreting information contained in hyperspectral images.

Remote sensing applications such as identification of vegetation, water bodies, and roads, as different landscapes have distinct spectral signatures.

MNF computes the principal components that maximize the signal-noise-ratio, rather than the variance. MNF transform is particularly efficient at deriving principal components from noisy band images. The principal component bands are spectrally distinct bands with low interband correlation.

Use the estimateAbundanceLS function to estimate the abundance maps for each endmember spectra.

Perform visual inspection and non-destructive testing operations, such as maturity monitoring of fruit. The comprehensive spectral data available in hyperspectral images enables precise and non-destructive analysis. For an example, see Predict Sugar Content in Grape Berries Using PLS Regression on Hyperspectral Data.

Hyperspectral imagemeaning

Identify materials in a hyperspectral image using a spectral library. For an example, see Endmember Material Identification Using Spectral Library.

Michelle Use to play on this forum, Back, New Mediums to explore. Dementia Caretaker struggling to be a real person again! Michelle Bousky Arts

I am new to lampworking but not to fusing. I have used E900 for metal to glass with good success. (Pin & earring backs to glass) I have also used silicone for glass to glass. (Mirrors and clock faces) Both could quality as “glopy” and take some time to set, particularly the silicone, but they did what I needed at the time. The epoxy I tried did not hold. Could be the type of epoxy or my mixing skills. Anyone know what large jewelry manufacturers use?

I was given a source for industrial UV glues(was able to procure some free samples to play with, various viscositys and cure times) — had to put things out on the porch in the sunlight to cure, even purchased a small UV light for curing fake nails from a beauty supply place. So far the UV glues seem to have the best bond but it’s definitely a finicky substance, you have to glue it and cure it quickly otherwise it never cures (highly annoying!).

For glass and metal, where the glass is not transparent, so no uv light can get through, the uv light-cured glue won’t work. I tried rear-view-mirror glue and it seems to work. I rough-up the metal a little with emory cloth so the glue can find grooves to stick to. I also rough-up the glass a little although that is likely not necessary. The glue is available in car parts stores and isn’t a lot. I save stuff that needs gluing until I have a bunch since one of the vials is glass and you break the glass to get the stuff out…obviously you can’t close it up again. The directions on the package say if you mess-up, don’t try to remove the review mirror part or you will crack the glass. I think that means that the “glue to glass” bond is at least as strong as the “glass to glass bond”. Anyway, I haven’t had any come loose…yet…knock-on-wood. I guess I consider Murphy an optimist and I want to keep the Druid Tree Gods happy. jim

Hi all! I’ve been playing with various types of glues to bond glass and metal together as well as glass to glass. I’m looking for suggestions from anyone else that’s played with this stuff.

Classify land cover by classifying each pixel in a hyperspectral image. For examples of classification, see these examples.

The false-color scheme uses a combination of any number of bands other than the visible red, green, and blue spectral bands. Use false-color representation to visualize the spectral responses of bands outside the visible spectrum. The false-color scheme efficiently captures distinct information across all spectral bands of hyperspectral data.

Hi Jen. I use E6000 (thick, yes!), to glue pin backs to the back of my painted, stained glass pins. I’ve never had one come apart in 7 years & I’m still wearing some of my original designs. I’ve also used it to glue glass pieces to base metal french barrettes, holds just fine. Now, I have pulled (intentionally) a pin back off of a piece of glass, though it’s not an easy thing to do. I’ve just tried GS Hypo Cement (think that’s what it’s called), didn’t like it, it was like water & I stuck my fingers together. Still have it stuck on one of my fingers & it was a week & a half ago. (also have a glob of E6000 that’s been stuck to my breakfast bar for over a year-it’s not going anywhere & funny, no one knows how it got there-teenagers!).

It seems I got this link from another thread on WC. It is a link that you can put what you want to glue together and you will get a recomendation on what to use. . . . I love the internet!

Hyperspectral image processing applications include land cover classification, material analysis, target detection, change detection, visual inspection, and medical image analysis.

Michelle Use to play on this forum, Back, New Mediums to explore. Dementia Caretaker struggling to be a real person again! Michelle Bousky Arts

Hyperspectral imagesoftware

"You're not obligated to win. You're obligated to keep trying to do the best you can every day." ~ Marian Wright Edelman, 2001

Heterogeneous surfaces are a combination of two or more distinct homogeneous surfaces. The pixels belonging to heterogeneous surfaces are known as mixed pixels. The spectral signature of a mixed pixel is a combination of two or more endmember signatures. This spatial heterogeneity is mainly due to the low spatial resolution of the hyperspectral sensor.

Spectral unmixing is the process of decomposing the spectral signatures of mixed pixels into their constituent endmembers. The spectral unmixing process involves two steps:

The values measured by a hyperspectral imaging sensor are stored to a binary data file by using band sequential (BSQ), band-interleaved-by-pixel (BIP), or band-interleaved-by-line (BIL) encoding formats. The data file is associated to a header file that contains ancillary information (metadata) like sensor parameters, acquisition settings, spatial dimensions, spectral wavelengths, and encoding formats that are required for proper representation of the values in the data file. Alternatively, the ancillary information can also be directly added to the data file as in TIFF and NITF file formats.

Thanks for all the replies gang! Sounds like the 2-part epoxy’s are something worth looking into, it’s the only thing I haven’t tried.

The RGB color scheme uses the red, green, and blue spectral band responses to generate the 2-D image of the hyperspectral data cube. The RGB color scheme brings a natural appearance, but results in a significant loss of subtle information.

I’m using it for the same application as Margaret – to attach sterling bails (a la Jinx) to end-of-mandrel pendants. I’ve had some hanging here for a little over a month now and they seem to be holding just fine!

To compensate for the atmospheric effects, you must first calibrate the pixel values, which are digital numbers (DNs). You must preprocess the data by calibrating DNs using radiometric and atmospheric correction methods. This process improves interpretation of the pixel spectra and provides better results when you analyse multiple data sets. In addition, spectral distortions which occur due to hyperspectral sensor characteristics during acquisition, can lead to inaccuracies in the spectral signatures. To enhance the reliability of spectral data for further analysis, you must apply preprocessing techniques that significantly reduce spectral distortions in hyperspectral images. For information about hyperspectral data correction methods, see Hyperspectral Data Correction.

Hyperspectral imagedataset

You can also use the spectral indices for change detection and threshold-based segmentation of hyperspectral images. To segment regions that cannot be distinguished using spectral indices, you can use spectral clustering approaches such as Simple Linear Iterative Clustering (SLIC) with the hyperslic function or anchor graphs with the hyperseganchor function.

In a hyperspectral image, the intensity values recorded at each pixel specify the spectral characteristics of the region that the pixel belongs to. The region can be a homogeneous surface or heterogeneous surface. The pixels that belong to a homogeneous surface are known as pure pixels. These pure pixels constitute the endmembers of the hyperspectral data.

Hyperspectralimaging camera

I just started playing with pendant-style beads that are done on the end of a mandrel… got too much inspiration from a beautiful floral pendant Kim Miles had on at the Gathering… I went to Ace, got a clear drying, 15 min-harden, two-part epoxy and tried it on three pieces. Don’t have pics yet, sorry… but it seems to have done the trick. I gave two to friends to test drive and I’ve been tuggin on one since Thursday, and no problems with any so far- feels like a very firm hold.

Use the nfindr function to estimate the endmembers by using the N-FINDR method. N-FINDR is an iterative approach that constructs a simplex by using the pixel spectra. The approach assumes that the volume of a simplex formed by the endmembers is larger than the volume defined by any other combination of pixels. The set of pixel signatures for which the volume of the simplex is high are the endmembers.

You can use the denoiseNGMeet function to remove noise from a hyperspectral data by using the non-local meets global approach.

Processing hyperspectral images of very large spatial resolution requires a large amount of system memory, and might cause MATLAB to run out of memory. You can crop a large hyperspectral image to a small region of interest, and then read only that small region into memory using the hypercube object and its functions. For an example of how to process small regions of large hyperspectral images, see Process Large Hyperspectral Images.

I just started playing with pendant-style beads that are done on the end of a mandrel… got too much inspiration from a beautiful floral pendant Kim Miles had on at the Gathering… I went to Ace, got a clear drying, 15 min-harden, two-part epoxy and tried it on three pieces. Don’t have pics yet, sorry… but it seems to have done the trick. I gave two to friends to test drive and I’ve been tuggin on one since Thursday, and no problems with any so far- feels like a very firm hold.

For hyperspectral image processing, the values read from the data file are arranged into a three-dimensional (3-D) array of the form M-by-N-by-C, where M and N are the spatial dimensions of the acquired data, C is the spectral dimension specifying the number of spectral wavelengths (bands) used during acquisition. Thus, you can consider the 3-D array as a set of two-dimensional (2-D) monochromatic images captured at varying wavelengths. This set is known as the hyperspectral data cube or data cube.

I finally found a glue that I like. It takes 24 hours to harden all the way. It is from Joanne’s and called, of all things, Glass and Bead Glue.

Jen – While it may be a little messy, epoxy should do the trick. Very strong bond, waterproof and available in a variety of forms, cure rates, etc… You can get it from water thin to very thick. Cure rates from 1 minute, to 5 minutes to 30 minutes to 2 hours. And if you are careful mixing it (it is two parts) you are guaranteed it will cure.

Orthogonal transforms such as principal component analysis (PCA) and maximum noise fraction (MNF), decorrelate the band information and find the principal component bands.

The hypercube function constructs the data cube by reading the data file and the metadata information in the associated header file. The hypercube function creates a hypercube object and stores the data cube, spectral wavelengths, and the metadata to its properties. You can use the hypercube object as input to all other functions in the Hyperspectral Imaging Library for Image Processing Toolbox™. You can use the Hyperspectral Viewer app to interactively visualize and process hyperspectral images.

The CIR color scheme uses spectral bands in the NIR range. The CIR representation of a hyperspectral data cube is particularly useful in displaying and analyzing vegetation areas of the data cube.

Hyperspectral imaging measures the spatial and spectral characteristics of an object by imaging it at numerous different wavelengths. The wavelength range extends beyond the visible spectrum and covers the spectrum from ultraviolet (UV) to long wave infrared (LWIR) wavelengths. The most popular are the visible, near-infrared, and mid-infrared wavelength bands. A hyperspectral imaging sensor acquires several images with narrow and contiguous wavelengths within a specified spectral range. Each of these images contains more subtle and detailed information. The different information in the various wavelengths is useful in diverse applications such as these.

The band selection approach uses orthogonal space projections to find the spectrally distinct and most informative bands in the data cube. Use the selectBands and removeBands functions for the finding most informative bands and removing one or more bands, respectively.

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Perform target detection by matching the known spectral signature of a target material to the pixel spectra in hyperspectral data. For examples of target detection, see Target Detection Using Spectral Signature Matching and Ship Detection from Sentinel-1 C Band SAR Data Using YOLOX Object Detection.