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 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.

A lot of research shows how populist and illiberal leaders are putting democracy in danger. But it rarely addresses what we feel is a more fundamental, underlying problem: severe political polarization.

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.

That finding gave us pause: it showed us that the potential for destructive divisions exists in almost all societies, even ones that seem relatively homogeneous. Our research underscores just how vulnerable democracies are to polarization—and how powerful the factors fueling divisions are.

Amplifying the effect of these divisive figures is the technologically fueled disruption of the media industry, especially the rise of social media. Opposition leaders often fan the flames as well by responding with antidemocratic and confrontational tactics of their own. In Turkey, for instance, the head of the main opposition party stoked tensions by calling on the military to oppose Erdoğan’s potential bid for the presidency in 2007.

Polarization is shaking societies across the world, from new democracies to long-established ones. Why are political divisions intensifying globally, and what can policymakers learn from other countries’ experiences?

The Democracy, Conflict, and Governance Program is a leading source of independent policy research, writing, and outreach on global democracy, conflict, and governance. It analyzes and seeks to improve international efforts to reduce democratic backsliding, mitigate conflict and violence, overcome political polarization, promote gender equality, and advance pro-democratic uses of new technologies.

And while the consequences of polarization are punishing, they don’t necessarily galvanize a government to respond, because the politicians who play the most significant role in exacerbating polarization mostly benefit from it and bear little of the cost.

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

For one, several promising efforts to limit polarization have focused on institutional reforms, such as decentralizing political power or changing electoral rules. Kenya, for instance, adopted a new constitution in 2010 that sought to ease ferocious competition for national office by giving regional officials greater autonomy and control over state resources. But important reforms don’t always require changing a country’s constitution: in the United States, for example, Maine passed legislation in 2016 to enact ranked-choice voting, a system that favors centrist candidates and discourages negative campaigning.

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.

Political leadership can also play a crucial role in de-escalating partisan divides. In Ecuador, President Lenín Moreno has rejected the polarizing tactics of his predecessor, even though the two come from the same political party. And in Turkey, opposition parties have achieved modest success by uniting to form a coalition: their candidate for mayor of Istanbul won a resounding victory in 2019 with a campaign that emphasized overcoming divisions.

Partisan conflict takes a heavy toll on civil society as well, often leading to the demonization of activists and human rights defenders. More seriously still, divisions can contribute to a spike in hate crimes and political violence: India, Poland, and the United States have all seen such increases in recent years.

Hyperspectral imagingsensor

Faced with Donald Trump’s return to the White House and his threat to transatlantic relations, the EU is woefully ill-equipped to act swiftly on foreign policy and security issues. An EU Security Council would go a long way in empowering it to respond more effectively.

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.

Our work identifies and analyzes eight different types of remedial actions, ranging from dialogue efforts and media reforms to international action. We’ll highlight just three examples here.

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 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 final distinctive and perhaps even unique feature of U.S. polarization is the powerful alignment of ethnicity, ideology, and religion on each side of the divide—what we call the “iron triangle” of U.S. polarization. In most other countries, just one or two of those three identity divisions is at the root of polarization; in the United States, all three are. As a result, America’s polarization is unusually encompassing and sharp.

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.

Hyperspectral imagingsoftware

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.

Polarization also reverberates throughout the society as whole, poisoning everyday interactions and relationships. Turkey is a particularly jarring example: almost eight out of ten people there would not want their daughter to marry someone who votes for the party they most dislike. Nearly three-quarters would not even want to do business with such a person.

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

The more we looked at the experiences of other divided democracies, the more we realized that U.S. polarization stands out as unusual. It has several distinctive features, and unfortunately, all of them spell trouble for U.S. democracy.

Intense partisanship has gripped the United States for an unusually long time and thus become ingrained in social and political life. Today’s divisions date back at least to the 1960s and have been steadily intensifying for over fifty years. Most other current cases of polarization are more recent in origin.

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.

While partisan warfare hasn’t eroded democracy in the United States to the same extent that it has in, say, Bangladesh or Turkey, it is testing our democratic guardrails in serious ways.

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.

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.

Yet despite these challenges, our research shows that a wide range of actors have tried inventive ways of addressing the problem—and sometimes achieved encouraging results.

In the first place, polarization in the United States isn’t primarily the result of polarizing politicians stoking divisions, as in most other countries. It has deep societal roots and is the outcome of a profound sociocultural struggle between contending conservative and progressive visions of the country. Consequently, U.S. polarization is not something that political leaders can easily reverse, even if they want to.

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.

Many other drivers of polarization struck us as surprising, even counterintuitive. You might expect, for instance, that a growing economy would ease polarization. Yet we found that in some places, such as India, it actually made things worse. Indeed, the growth of India’s middle class has led to rising support for polarizing Hindu nationalist narratives.

Once a society becomes deeply divided, it is very difficult to heal. Before talking about remedial actions, it’s crucial to understand why this problem is so thorny and difficult to overcome.

Take the example of Bangladesh: acrimonious political competition there has led to violence, election fraud, and a complete breakdown of democracy. But polarization isn’t rooted in any fundamental ethnic, ideological, or religious division among voters. It is almost entirely the result of power struggles within a political elite that plays up and manufactures divisions.

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.

Polarization tends to escalate at a dizzyingly fast pace, often in the span of just a few years. Just look at how rapidly the 2016 Brexit referendum has ripped the United Kingdom apart.

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

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.

It routinely undermines the independence of the judiciary, as politicians attack the courts as biased or pack them with loyalists. It reduces legislatures either to gridlock or to a rubberstamp function. In presidential systems, it frequently leads to the abuse of executive powers and promotes the toxic view that the president represents only his or her supporters, rather than the country as a whole.

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.

We also found that patronage and corruption—two decidedly antidemocratic practices—can temporarily reduce polarization by helping politicians build very big tents. In the long term, however, the political rot that this causes frequently leaves voters disgusted with the traditional parties and fuels the rise of divisive populist figures, like Hugo Chávez in Venezuela and Jair Bolsonaro in Brazil.

These consequences generate a vicious cycle of rising polarization. Attacks on the judiciary, for example, only diminish its capacity to arbitrate conflict and heighten distrust between the opposing sides.

Hyperspectral imagingcamera

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.

We wanted to know: Why has polarization come to a boil in so many places in recent years? Are there any telling similarities in the patterns of polarization across different countries? And perhaps most importantly, once societies have become deeply polarized, what can they do to start healing their divisions?

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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.

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.

Particularly striking was just how decisive polarizing leaders often are. Figures like Narendra Modi in India, Jarosław Kaczyński in Poland, and Recep Tayyip Erdoğan in Turkey have relentlessly inflamed basic divisions and entrenched them throughout society (often with resounding electoral success). They’ve aggravated tensions not only by demonizing opponents and curtailing democratic processes but also by pushing for radical changes—like a total ban on abortion in Poland.

Hyperspectral imagingcamera price

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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.

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.

Other efforts have involved legal or judicial action to limit polarization and majoritarianism—the idea that the feelings and rights of the minority should not constrain leaders with majority support. In India, for example, the Supreme Court has spoken out in defense of democratic institutions and demanded greater accountability for hate crimes and political violence.

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.

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

Hyperspectral imagingapplications

Perhaps most fundamentally, polarization shatters informal but crucial norms of tolerance and moderation—like conceding peacefully after an electoral defeat—that keep political competition within bounds.

Still, these initiatives are small compared to the larger forces driving polarization. Democracies will need to rise to this challenge in new and determined ways if they are to swim successfully against the swelling global current of polarization.

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

How doeshyperspectral imagingwork

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

Hyperspectral imagingin agriculture

From these, we extracted cross-cutting findings. And the sheer diversity of our cases—in terms of societal makeup, political institutions, and economic development—opened our eyes to discoveries that we might have missed if we had looked only at the United States and Europe.

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.

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

We focused on nine diverse countries grappling with the problem: Bangladesh, Brazil, Colombia, India, Indonesia, Kenya, Poland, Turkey, and the United States. We assembled a group of scholars with deep local expertise on these countries, and they produced in-depth case studies.

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.

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.

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.

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 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.

Hyperspectral imagingsatellite

Polarization is tearing at the seams of democracies around the world, from Brazil and India to Poland and Turkey. It isn’t just an American illness; it’s a global one.

Now is the moment to develop more sustainable security mechanisms in Palestine. The proven practice of unarmed civilian protection and accompaniment is a critical initiative toward such security.

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.

The degree of similarity we found across countries was startling. Even in democracies as different as Colombia, Kenya, and Poland, many of the roots, patterns, and drivers of polarization were the same.

The Democracy, Conflict, and Governance Program is a leading source of independent policy research, writing, and outreach on global democracy, conflict, and governance. It analyzes and seeks to improve international efforts to reduce democratic backsliding, mitigate conflict and violence, overcome political polarization, promote gender equality, and advance pro-democratic uses of new technologies.

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

When we looked at the fierce polarization in many countries, we expected to find deep-seated differences between the opposing sides. So we were taken aback to discover that sometimes those differences seem slight.

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.

Polarization then entrenches itself and becomes self-perpetuating. Polarizing actions and reactions feed on each other, dragging countries into a downward spiral of anger and division.

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

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.

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.