Coastal | 400 – 452 nm Absorbed by chlorophyll in healthy plants and aids in conducting vegetative analysis Least absorbed by water, and will be very useful in bathymetric studies Substantially influenced by atmospheric scattering and has the potential to improve atmospheric correction techniques Yellow – Blue – CoastalBathymetry      Applications Include: Coastal applicationsWater penetrationDeepwater masksMaterial differentiationShadow-tree-water differentiation

Multispectralcamera uses

Through our strategic partnerships, European Space Imaging offers hyperspectral imagery with resolutions as high as 5 m. The key factor that separates hyperspectral imagery is the width of the spectral bands. With hundreds of bands covering VNIR and SWIR spectrums, analytics become much more precise.

Multispectralimaging skin

Blue | 448 – 510 nm Readily absorbed by chlorophyll in plants Provides good penetration of water Less affected by atmospheric scattering and absorption compared to the Coastal band Green – Blue – CoastalWater Depth Composite      Applications Include: Coastal applicationsWater body penetrationDiscrimination of soil/vegetationForest typesReef cover features

Near-Infrared 2 | 866 – 954 nm Overlaps the NIR1 band but is less affected by atmospheric influenceEnables broader vegetation analysis and biomass studies NIR2 – Yellow – CoastalCrop Species Differentiation      Applications Include: Biomass surveysPlant stressCrop TypeMaterial identification

Means by which most of the magnification is achieved in a compound microscope, as found directly above the specimen and typically separated into 3 or 4 different magnifications per microscope.

Multispectralcamera for agriculture

In this model, ResNet34 is used for feature extraction and the FCN operation remains as is. The feature of ResNet architecture is exploited where just like VGG, as the number of filters double, the feature map size gets halved. This gives a similarity to VGG and ResNet architecture while supporting deeper architecture and addressing the issue of vanishing gradients while also being faster. The fully connected layer at the output of ResNet34 is not used and instead converted to fully convolutional layer by means of 1×1 convolution.

Multispectralremote sensing

Only a small number of space-based imagery providers offer hyperspectral imagery, though the potential for industry disrupting applications are already making headway across the globe.

Figure legend: The objective lenses can be found immediately above the mechanical stage. Note that there are four of them, though here only three are fully visible from this angle, with magnifications of 4×, 10×, 40×, and 100×.

Green | 518 – 586 nm Able to focus more precisely on the peak reflectance of healthy vegetation Ideal for calculating plant vigor Very helpful in discriminating between types of plant material when used in conjunction with the Yellow band Coastal – Blue – GreenReef Water Depth      Applications Include: Crop typesSea grass and reefsBathymetry

Red | 632 – 692 nm Focused on the absorption of red light by chlorophyll in healthy plant materials One of the most important bands for vegetation discrimination Very useful in classifying bare soils, roads, and geological features NIR1 – Red – GreenTree Species Identification      Applications Include: Chlorophyll absorptionVegetation analysisPlant species Plant stress

Multispectralimage

UNet architecture for semantic segmentation with VGG16 as the encoder or feature extractor. VGG16 is used as an encoder or feature extractor in the contracting path and the corresponding symmetric expanding path predicts the dense segmentation output.

The power to see beyond the visible light spectrum cannot be overstated. With hundreds of current applications and countless more waiting to be discovered, multispectral imagery is the key to unlocking insights in any industry. European Space Imaging captures imagery with up to 8 multispectral bands plus the ability to collect imagery in Shortwave Infrared (SWIR) and hyperspectral as well.

Red Edge | 706 – 746 nm Centered strategically at the onset of the high reflectivity portion of vegetation responseVery valuable in measuring plant health and aiding in the classification of vegetation NIR1 – Red Edge – RedCamouflage Detection      Applications Include: Vegetation healthVegetation stressVegetation typeVegetation ageSea grass and reefsLand / no land analysisImpervious surfacesTurbidityCamouflage

Multispectralvs hyperspectral

Short-Wave Infrared | 1184 – 2373 nm Focuses deeper into the infrared spectrum Able to detect heat Detection of materials containing anion groups such as Al-OH, Mg-OH, Fe-OH, Si-OH, carbonates, ammonium, and sulfates S3 – S6 – S8Clay Mineral Alteration      Applications Include: Fire / Valance eruption responseMaterial identificationSoil moistureMineral content

In this model, VGG16 is used for feature extraction which also performs the function of an encoder. The fully connected layer of the VGG16 is not used and instead converted to fully convolutional layer by means of 1×1 convolution.

MultispectralCamera price

Multispectralcamera for drone

Focusing a microscope typically will involve starting with a lower magnification objective and then working ones way up to a higher magnification. A common mistake is to not follow this sequence, either never bringing magnification up to a high level or, more commonly, to skip attempting to first focus at the lower levels of magnification. Note that it often is possible to skip the lowest level of magnification. If you are having difficulty focusing your microscope onto the specimen, however, then one thing you might try is to start over at the lowest magnification.

Near-Infrared 1 | 772 – 890 nm Very effective for the estimation of moisture content and plant biomass Effectively separates water bodies from vegetation, identifies types of vegetation and also discriminates between soil types NIR1 – Red – GreenVegetation Analysis      Applications Include: Biomass surveysPlant stressWater body delineationSoil moisture

Yellow | 590 – 630 nm Very important for feature classification Detects the “yellowness” of particular vegetation, both on land and in the water NIR2 – Yellow – BlueRoof Material Identification      Applications Include: Leaf ColourationPlant StressCO2 concentrationMaterial identificationAlgal bloomsSea grass and reefsSeparation of iron formations“True colour”Plant species identification

UNet architecture for semantic segmentation with ResNet34 as encoder or feature extraction part. ResNet34 is used as an encoder or feature extractor in the contracting path and the corresponding symmetric expanding path predicts the dense segmentation output.

The lens (or lenses) that sit immediately above the specimen (as found on a slide) is called the objective lens (or lenses). The reason for the name is that the specimen is also known as the object and the objective lens is immediately adjacent to the object (rather than immediately adjacent to one's eye or eyes) The pathway of light is: light source → condenser → iris diaphragm → stage → object/specimen → objective lens → ocular lens → eye or camera. The objective lens will have certain associated magnifications such as 4×, 10×, 40×, and 100×. See also Parfocal.