Scratch Dig Inspection Process by Semrock Technical Team - scratch dig

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Source: Meng, Y.; Cao, B.; Mao, P.; Dong, C.; Cao, X.; Qi, L.; Wang, M.; Wu, Y. Tree Species Distribution Change Study in Mount Tai Based on Landsat Remote Sensing Image Data. Forests 2020, 11, 130. https://doi.org/10.3390/f11020130 (CC BY 4.0)

The advantage of hyperspectral remote sensing lies in the acquisition of an almost continuous reflectance spectrum for each pixel in an image. The narrow bands enable the detection of slight differences in features, which would otherwise be undetected using the relatively broad wavelength bands of multispectral imagery.

Vegetation appears green because it reflects the visible green portion of the electromagnetic spectrum but absorbs the visible red and blue portions

Spectral signature is the amount of incident energy reflected by a material over different portions of the electromagnetic spectrum. We usually represent spectral signatures as a graph (a spectral response curve) showing the variation in reflected energy across different wavelengths.

The purposes of optical microscopes are broadly classified into two; "biological-use" and "industrial-use". Using this classification method, objective lenses are classified into "biological-use" objectives and "industrial-use" objectives. A common specimen in a biological use is fixed in place on the slide glass, sealing it with the cover glass from top. Since a biological-use objective lens is used for observation through this cover glass, optical design is performed in consideration of the cover glass thickness (commonly 0.17mm). Meanwhile, in an industrial use a specimen such as a metallography specimen, semiconductor wafer, and an electronic component is usually observed with nothing covered on it. An industrial-use objective lens is optically designed so as to be optimal for observation without any cover glass between the lens end and a specimen.

For vegetation, the rate of increase in reflectance is the highest in the region between the visible red and near-infrared bands. This region is known as the red edge band.

Aside from mineral exploration, multispectral imagery also facilitates the environmental monitoring of mining areas. Discover how geospatial technology enables mine operators to protect the environment and plan remediation throughout the mine lifecycle.

In this section, we look at the applications of multispectral imagery and point out where hyperspectral imagery might come in. In a bid to show the importance of different portions of the electromagnetic spectrum, we outline those that are relevant for each application.

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Objective lenses are roughly classified basically according to the intended purpose, microscopy method, magnification, and performance (aberration correction). Classification according to the concept of aberration correction among those items is a characteristic way of classification of microscope objectives.

The spectral response pattern differs among different plant species enabling species recognition using remotely sensed imagery.

The major difference between multispectral and hyperspectral images is the number of bands within the same portion of the electromagnetic spectrum.Â

There are lots of rocks and minerals on the surface of the earth. Each has unique reflectance properties determined by color, texture, chemical composition, etc.

A variety of microscopy methods have been developed for optical microscopes according to intended purposes. The dedicated objective lenses to each microscopy method have been developed and are classified according to such a method. For example, "reflected darkfield objective (a circular-zone light path is applied to the periphery of an inner lens)", "Differential Interference Contrast (DIC) objective (the combination of optical properties with a DIC( Nomarski）prism is optimized by reducing lens distortions)", "fluorescence objective (the transmittance in the near-ultraviolet region is improved)", "polarization objective (lens distortions are drastically reduced)", and "phase difference objective (a phase plate is built in) are available.

Multispectral imagery plays a vital role in the initial stages of mineral exploration: enabling discrimination and mapping of different rocks and minerals based on their reflectance in the visible and infrared portions of the spectrum.

Because of their broad spectral bands, multispectral imagery is limited in the detailed classification of rocks and minerals---not to mention estimating the distribution of mineral constituents within a pixel.

The spectral reflectance of soils varies by their physical and chemical properties. These interactions give rise to multispectral data applications for studying soil properties like moisture content, organic matter content, iron content, texture, roughness, and mineral composition.Â

Multispectralimagingsatellites

From a visual inspection, we can infer that the site has vegetation cover because of the green color. However, we cannot distinguish the vegetation speciesâlet alone detect changes in their distribution between the two years.Â This is because different vegetation species have almost similar reflectance within the visible range of the electromagnetic spectrum: they reflect green light.

Note: The spectral signature of an object is not constant. It varies based on, among other things, the physical properties of the material, atmospheric effects, and the sensor's view angle. Therefore, spectral response curves are sometimes drawn as shadingsâas opposed to a thin lineâto show variability.

Hyperspectral vs multispectral

Polarization states are mapped to the Poincaré sphere using an approach similar to the system of latitude and longitude used to locate points on the Earth's ...

Determination of soil organic matter content is important for soil management, estimation of available nutrients for precision agriculture, and climate variability studies.

Hyperspectral imagery, therefore, improves the identification and quantitative assessment of the physical and chemical properties of the objects of interest, e.g., vegetation, water, soils, minerals, etc.Â

Aside from water quality, multispectral imagery also supports the monitoring of ocean environments. Discover why satellite imagery is the key to ocean monitoring.Â

That said, depending on water characteristics like depth, suspended sediments, and aquatic vegetation, water bodies have unique reflectance properties. Multispectral imagery is thus useful in examining these variable water environments.

An objective lens is the most important optical unit that determines the basic performance/function of an optical microscope To provide an optical performance/function optimal for various needs and applications (i.e. the most important performance/function for an optical microscope), a wide variety of objective lenses are available according to the purpose.

Multispectralimagingin agriculture

Plant water content and biochemistry (e.g., protein and cellulose content) influences reflectance in the shortwave infrared regionÂ (1000--2500 nm)

When light hits vegetation, reflection, absorption, or transmission occurs. These are influenced by the leaves' chemical, structural, and biological properties. This interaction between plants and light enables the discovery of plant properties. In summary:

R Paschotta · 2 — A light beam is linearly polarized, which means that the electric field oscillates in a certain linear direction perpendicular to the beam axis.

Multispectralimagingarchaeology

Multi spectral imagingcamera

The following table contains examples of hyperspectral sensors, along with details such as number of bands, swatch, and resolution.

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Difference between multispectral and hyperspectral remote sensing

When a plant is unhealthy or under stress, it produces less chlorophyll. Less chlorophyll means higher reflectance in the visible red region (chlorophyll absorbs visible radiation, especially in the blue and red wavelengths). At the same time, as the water content in leaves decreases, reflectance in the near-infrared region increases. These changes alter the position and shape of the red edge band.

Multispectral and hyperspectral images reveal insights about our changing world beyond our human experience. They tell us about the mineral composition of the soil, biomass and plant health, surface roughness, vegetation types, ocean temperatures, and so much more.

Spectral reflectance curves of bermudagrass under different levels of water deprivation. (Source: Caturegli, L., Matteoli, S., Gaetani, M. et al. Effects of water stress on spectral reflectance of bermudagrass. Sci Rep 10, 15055 (2020). https://doi.org/10.1038/s41598-020-72006-6) (CC BY 4.0)

For example, the image below illustrates changes in the shape and position of the red-edge band for plants at three different levels of water stress.

For example, the graph below shows the spectral response patterns of conifer, deciduous, and grass vegetation. While their reflectance in the visible range is almost alike, they display different reflectance patterns in the near-infrared range enabling their classification.

Variation of soil reflectance with water content. (Source: Li, T.; Mu, T.; Liu, G.; Yang, X.; Zhu, G.; Shang, C. A Method of Soil Moisture Content Estimation at Various Soil Organic Matter Conditions Based on Soil Reflectance. Remote Sens. 2022, 14, 2411. https://doi.org/10.3390/rs14102411) (CC BY 4.0)

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Multispectral images in Remote sensing

Spectral signatures of various materials. (Yang, Q.; Liu, X.; Wu, W. A Hyperspectral Bidirectional Reflectance Model for Land Surface. Sensors 2020, 20, 4456. https://doi.org/10.3390/s20164456) (CC BY 4.0)

Studies have shown that soil reflectance properties in different multispectral bands can be used to estimate soil organic matter. For example, an increase in organic matter (even amounts only exceeding 2%) has a significant effect on soil reflectance. Specifically, organic matter reduces overall soil reflectivity up to an organic matter content of 5% beyond which there is no more change.

Soil reflectance in the visible wavelengths decreases with increasing soil moisture up to the point of saturation---where there is no more change. Similarly, soil reflectance in the near-infrared region decreases with increasing moisture. These reflectance characteristics allow for the determination of moisture content using multispectral bands.

Hyperspectral images contain more information on subtle spectral features of vegetation than multispectral images. They are thus useful for discriminating between plant varieties, detecting vegetation stress, and even estimating biomass in higher detail.

Additionally, in some cases, the construction of vegetation indices using the narrow bands of hyperspectral imagery yields better estimates of biophysical parameters (e.g., biomass and leaf area index) than vegetation indices derived from multispectral imagery.

Meanwhile, an objective lens for which the degree of chromatic aberration correction to the secondary spectrum (g ray) is set to medium between Achromat and Apochromat is known as Semiapochromat (or Flulorite).

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Multispectralimagingskin

Different portions of the spectrum are relevant for providing information about various features on the Earth's surface. But we first have to identify which band or bands to use. Which calls for an understanding of spectral signatures.

Of the total energy radiated by objects around us, we can only see a small part. Our eyes can only perceive the visible range of the electromagnetic spectrum.

Wavelength ranges of hyperspectral imaging systems. (Source: Xie, Y.; Plett, D.; Liu, H. The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat. AgriEngineering 2021, 3, 924-941. https://doi.org/10.3390/agriengineering3040058) (CC BY 4.0)

Source: Vangi, E.; D'Amico, G.; Francini, S.; Giannetti, F.; Lasserre, B.; Marchetti, M.; Chirici, G. The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination. Sensors 2021, 21, 1182. https://doi.org/10.3390/s21041182 (CC BY 4.0)

Human vision is constrained to the visible portion of the electromagnetic spectrum. Remote sensing systems, however, provide the opportunity to measure beyond the visible and into the full spectrum.

Some satellite sensors can detect visible and nonvisible portions of the electromagnetic spectrum. Such sensors are called multispectral or hyperspectral sensors, and the resulting images are multispectral or hyperspectral imagery.

Multispectral imagery is imagery containing multiple spectral bands of the electromagnetic spectrum. They are collected by sensors that measure reflected energy in specific portions of the electromagnetic spectrum.

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The electromagnetic spectrum is the entire range of wavelengths of electromagnetic radiation. For analytical purposes, the electromagnetic spectrum is divided into bands (portions), each representing a range of wavelengths. We refer to the bands by name: gamma rays, x-rays, ultraviolet rays, visible light (0.4Âµm--0.7Âµm), infrared (0.7Âµm--100Âµm), microwaves (1mm--1m), and radio waves.

Most multispectral imagery contains 4â12 bands in the visible to the infrared portions of the electromagnetic spectrum. According to a USGS report, multispectral images may contain up to 36 wavelength bands.

Water only reflects up to approximately 10% of incident energy. Of these, most are in the visible portion of the electromagnetic spectrum, with little or no reflectance in the near-infrared region.

The above example demonstrates one of the advantages of being able to see beyond the visible spectrum. Luckily, we do not have to depend on our eyes for this.

For example, the video below shows how Landsat 8 multispectral imagery helped scientists identify algal bloom in Lake Utah:

Water quality is assessed using several indicators including dissolved minerals, suspended material, bacteria levels, oxygen levels, salinity, etc. These indicators affect the optical properties of water in the visible and near-infrared portions, and their identification can therefore benefit from remote sensing techniques.

Typical spectral signature of a green leaf and factors influencing the curve. (Source: Moroni, M.; Porti, M.; Piro, P. Design of a Remote-Controlled Platform for Green Roof Plants Monitoring via Hyperspectral Sensors. Water 2019, 11, 1368. https://doi.org/10.3390/w11071368) (CC BY 4.0)

An optical microscope is used with multiple objectives attached to a part called revolving nosepiece. Commonly, multiple combined objectives with a different magnification are attached to this revolving nosepiece so as to smoothly change magnification from low to high only by revolving the nosepiece. Consequently, a common combination lineup is comprised from among objectives of low magnification (5x, 10x), intermediate magnification (20x, 50x), and high magnification (100x). To obtain a high resolving power particularly at high magnification among these objectives, an immersion objective for observation with a dedicated liquid with a high refractive index such as immersion oil or water charged between the lens end and a specimen is available. Ultra low magnification (1.25x, 2.5x) and ultra high magnification (150x) objectives are also available for the special use.

Hyperspectral imagery contains many more wavelength bands collected in narrow, adjacent sections across the visible and infrared regions of the electromagnetic spectrum.

Axial chromatic aberration correction is divided into three levels of achromat, semiapochromat (fluorite), and apochromat according to the degree of correction. The objective lineup is divided into the popular class to high class with a gradual difference in price. An objective lens for which axial chromatic aberration correction for two colors of C ray (red: 656,3nm) and F ray (blue: 486.1nm) has been made is known as Achromat or achromatic objective. In the case of Achromat, a ray except for the above two colors (generally violet g-ray: 435.8nm) comes into focus on a plane away from the focal plane. This g ray is called a secondary spectrum. An objective lens for which chromatic aberration up to this secondary spectrum has satisfactorily been corrected is known as Apochromat or apochromatic objective. In other words, Apochromat is an objective for which the axial chromatic aberration of three colors (C, F, and g rays) has been corrected. The following figure shows the difference in chromatic aberration correction between Achromat and Apochromat by using the wavefront aberration. This figure proves that Apochromat is corrected for chromatic aberration in wider wavelength range than Achromat is.

The number of multispectral bands (top) compared to the number of hyperspectral bands (bottom) in the same portion of the electromagnetic spectrum.

The application of fine spectral resolution hyperspectral images, however, enables the identification of a large range of minerals and allows for the determination of their composition and abundance.

Photography or image pickup with a video camera has been common in microscopy and thus a clear, sharp image over the entire field of view is increasingly required. Consequently, Plan objective lenses corrected satisfactorily for field curvature aberration are being used as the mainstream. To correct for field curvature aberration, optical design is performed so that Petzval sum becomes 0. However, this aberration correction is more difficult especially for higher-magnification objectives. (This correction is difficult to be compatible with other aberration corrections) An objective lens in which such correction is made features in general powerful concave optical components in the front-end lens group and powerful concave ones in the back-end group.

In the optical design of microscope objectives, commonly the larger is an N.A. and the higher is a magnification, the more difficult to correct the axial chromatic aberration of a secondary spectrum. In addition to axis chromatic aberration, various aberrations and sine condition must be sufficiently corrected and therefore the correction of the secondary spectrum is far more difficult to be implemented. As the result, a higher-magnification apochromatic objective requires more pieces of lenses for aberration correction. Some objectives consist of more than 15 pieces of lenses. To correct the secondary spectrum satisfactorily, it is effective to use "anomalous dispersion glass" with less chromatic dispersion up to the secondary spectrum for the powerful convex lens among constituting lenses. The typical material of this anomalous dispersion glass is fluorite (CaF2) and has been adopted for apochromatic objectives since a long time ago, irrespective of imperfection in workability. Recently, optical glass with a property very close to the anomalous dispersion of fluorite has been developed and is being used as the mainstream in place of fluorite.

The visible portionâwhat we perceive with our eyesâis further segmented into blue, green, and red regions. Similarly, the infrared band is segmented into near-infrared, mid-infrared, and far-infrared regions.

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The above spectral response characteristics aid in the development of vegetation indices, which are useful for the extraction of vegetation traits from multispectral and hyperspectral data. They also give rise to the following applications of multispectral and hyperspectral imagery for vegetation analyses.

Multispectral imagery is useful for the differentiation of vegetation types, soils, water, and human-made structures. Owing to their higher spectral resolution, hyperspectral imagery enables a finer scale distinction of features. Hyperspectral imagery is especially useful for uncovering spectral details which cannot be detected by multispectral sensors.

Further, examining spectral response characteristics aids in the development of indices that are used to quantify various feature characteristics. An example is the Normalized Difference Vegetation Index (NDVI) used for determining areas with vegetation cover and evaluating their condition.

Beyond the visible range, however, vegetation species have different reflectance characteristics making them easier to differentiate. In this example, therefore, using reflectance characteristics of vegetation beyond the visible range allows us to classify different tree species and examine changes in their distribution (as in the maps below).Â

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an ophthalmoscope with a layer of water to neutralize the refraction of the cornea.