Identification of Geospatial Objects Using Spectral Pattern

2019 ◽  
pp. 882-893
Author(s):  
Subhabrata Barman
Keyword(s):  
Spectral Reflectance ◽  
Target Surface ◽  
Classification Rules ◽  
Spectral Image ◽  
Incident Wavelength ◽  
Reflectance Curve ◽  
Spectral Pattern ◽  
Multispectral Remote Sensing ◽  
Unique Signature ◽  
Absorption Characteristics

Solar radiation on hitting a target surface may be transmitted, absorbed or reflected. Different materials reflect and absorb differently at different wavelengths. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as a function of the incident wavelength and serves as a unique signature for the material. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish its spectrum from those of other materials. This premise provides the basis for multispectral remote sensing. Nguyen Dinh Duong (1997) proposed a method for decomposition of multi-spectral image into several sub-images based on modulation (spectral pattern) of the spectral reflectance curve. The hypothesis roots from the fact that different ground objects have different spectral reflectance and absorption characteristics which are stable for a given sensor. This spectral pattern can be considered as invariant and be used as one of classification rules.

Identification of Geospatial Objects Using Spectral Pattern

2017 ◽  
pp. 1-15
Author(s):  
Subhabrata Barman
Keyword(s):  
Spectral Reflectance ◽  
Target Surface ◽  
Classification Rules ◽  
Spectral Image ◽  
Incident Wavelength ◽  
Reflectance Curve ◽  
Spectral Pattern ◽  
Multispectral Remote Sensing ◽  
Unique Signature ◽  
Absorption Characteristics

Solar radiation on hitting a target surface may be transmitted, absorbed or reflected. Different materials reflect and absorb differently at different wavelengths. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as a function of the incident wavelength and serves as a unique signature for the material. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish its spectrum from those of other materials. This premise provides the basis for multispectral remote sensing. Nguyen Dinh Duong (1997) proposed a method for decomposition of multi-spectral image into several sub-images based on modulation (spectral pattern) of the spectral reflectance curve. The hypothesis roots from the fact that different ground objects have different spectral reflectance and absorption characteristics which are stable for a given sensor. This spectral pattern can be considered as invariant and be used as one of classification rules.

An optical anomaly possibly due to optical activity in some uniaxial opaque ore minerals

1980 ◽  
Vol 43 (331) ◽  
pp. 909-912 ◽  
Author(s):  
A. J. Hall ◽  
B. D. Cervelle ◽  
P. R. Simpson
Keyword(s):  
Optical Activity ◽  
Spectral Reflectance ◽  
Optical Constants ◽  
Optically Active ◽  
Maximum Effect ◽  
Polished Section ◽  
Reflectance Curve ◽  
Visible Part ◽  
Ore Minerals ◽  
Reflectance Measurements

SummarySpectral reflectance measurements on three uniaxial ore minerals, tellurium, chalcopyrite, and stibioluzonite, which are opaque at least in the visible part of the spectrum have revealed that the reflectance curve of the ordinary ray varies with crystallographic orientation of the polished section. The three minerals possess symmetries capable of exhibiting optical activity in transmitted light. A possible explanation, therefore, of the anomalous behaviour is that the optical constants, i.e. the refractive index and the absorption coefficient, and thus also the reflectance, of the ordinary ray may differ for sections cut normal to c where optical activity probably has its maximum effect and for sections cut parallel to c where there is probably little or no complication due to optical activity. There would therefore appear to be a need to extend the theory of reflection from absorbing media to include reflection from optically active absorbing minerals.

scholarly journals Creation of Laboratory Spectral Library of Beryl in Sevapure Area, Kadavur Basin, Karur District, Tamilnadu

2019 ◽  
Vol 9 (1) ◽  
pp. 5325-5327
Keyword(s):  
Spectral Reflectance ◽  
Spectral Library ◽  
Reflectance Curve ◽  
Aster Data ◽  
Value Changes ◽  
High Absorption

The study aims to create a laboratory spectral library for the mineral beryl by using the spectroradiometer instrument and the Envi software. The mineral collected from Sevapure area of Kadavur Basin. The laboratory spectra were created and matched with the USGS spectral library. For the study of the spectra an imagery file platform is needed and the ASTER data used as the platform. The laboratory spectra formed between 0.556 to 2.4-micrometre wavelengths. The spectral reflectance curve shows the reflectance values from 0.09 to 0.33 micrometres and the high absorption takes place at 0.80 μm. The USGS spectra show the same value at the wavelength and the reflectance value changes as 0.55. Both shows maximum reflectance at 1.00 and the wavelength at 1.65 μm. The spectral library created has used for further studies.

scholarly journals Hyper Spectral Image Characteristics of Aronia Melanocarpa Leaves under Saline Alkali Stress

2020 ◽  
Keyword(s):  
Spectral Reflectance ◽  
Near Infrared ◽  
Spectral Information ◽  
Alkali Stress ◽  
Plant Leaves ◽  
Infrared Band ◽  
Spectral Image ◽  
Aronia Melanocarpa ◽  
Image Characteristics ◽  
Hyper Spectral

<p>Objective: To obtain the characteristics analysis results of aronia melanocarpa leaves under saline alkali stress state and improve yield and quality of aronia melanocarpa. Methods: Using hyper spectral imaging system to obtain hyper spectral images of aronia melanocarpa leaves under saline alkali stress. The clear hyper spectral image is obtained by the conversion of reflectance, spectral envelope removal, and spectral denoising, and final hyper spectral image is obtained by the normalization of a clearer hyper spectral image. Results: Spectral information of aronia melanocarpa leaves under slight saline alkali stress: in the visible band, leaf reflectance is lower than leaf nutrient rich stress condition; in the near infrared band, the nutrient rich leaves was significantly higher than that of stress leaves. Spectral information of leaves under moderate saline alkali stress: spectral reflectance of different lesion spots for a same leaf in 550-680nm band is: severe &gt; moderate &gt; slight &gt; normal, in the near infrared band is on the contrary; spectral reflectance for different lesion grades in 550-680 nm, severe lesion leaves have highest reflectance, and normal leaves have the lowest reflectance. Under severe saline alkali stress, the leaf spectral information is: there is no significant difference in leaf spectral reflectance between the attachment aphid and the damaged leaf, but the difference is obvious for normal leaves in the band of 450-500 nm, 560-680 nm and 750-900 nm. Comparative results analysis for the three of saline alkali stress degree is: the near infrared band of 560-680 nm and the visible band of 780-900 nm is the sensitive band for the diagnosis of three kinds of stress; slight saline alkali stress has the most significant differences at 550 nm, and 780-900 nm; severe saline alkali stress has at 680 nm and 780-900 nm. Conclusion: The proposed method can analyze hyper spectral image characteristics of aronia melanocarpa leaves under different saline alkali stress the condition of is a kind of plant leaves, which is an efficient method for the analysis of characteristics of plant leaves under saline alkali stress.</p>

scholarly journals Multi-Spectral Image Segmentation Based on the K-means Clustering

2019 ◽  
Vol 9 (2) ◽  
pp. 1016-1019 ◽  
Keyword(s):  
Remote Sensing ◽  
Minimum Distance ◽  
Clustering Algorithm ◽  
Satellite Image ◽  
Vital Role ◽  
Spectral Image ◽  
Multispectral Remote Sensing ◽  
High Resolution Satellite Imagery ◽  
Crop Health ◽  
Sentinel 2

Agriculture is one of the oldest economic aspects of human civilisation, and it is still undergoing a dynamic makeover in the course of the application of IT innovative mechanisms in farming methodology. Remote sensing has vied a significant role in crop classification, crop health and yield assessment. Multispectral remote sensing plays a vital role in providing enhancement of more detailed analysis of crop segmentation. In this article, pixel-based clustering of 12 channels is carried out using the satellite image from Sentinel 2 remote sensing satellite via k-means clustering. K-means clustering algorithm is usually a better method of classifying high-resolution satellite imagery. The extracted regions are classified using a minimum distance decision rule.

Sign in / Sign up

Export Citation Format

Share Document