Improved pure pixel identification algorithms to determine the endmembers in hyperspectral images

Jasmine, S. Graceline; Pattabiraman, V.

doi:10.1016/j.compeleceng.2018.07.023

Cited by 5 publications

(10 citation statements)

References 22 publications

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“…In the case of radiometric calibration, the radiance TOA (Top of Atmosphere) R [W/(m . sr. μm)] is calculated by (1), with the digital numbers of the image to correct the influence of the solar zenith angle between the acquisition of the information. The value of the gain coefficients G and compensation factor O is obtained from the metadata.…”

Section: B Radiometric and Atmospheric Correctionmentioning

confidence: 99%

“…Principal component analysis (PCA) presents the difficulty of not segregating adjacent noise in the dataset [24]. This problem is solved by applying a transformation MNF or also called cascading PCA [1]. Its most notable difference is that MNF considers noise within the data set, while PCA only considers the variations of each vector [25].…”

Section: Minimum Noise Fraction (Mnf)mentioning

confidence: 99%

“…The development of instruments for Earth observation, such as satellites, has made it possible to obtain more accurate information about the topographic reality of the planet. Remote Sensing allows studying a geographic object using data represented in a satellite image [1]. This technique is very useful for monitoring changes in land cover [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pixel Purity Index Applied to the Mapping of Degraded Soils by the Presence of Cangahuas in the Ilaló Volcano, Ecuador

Palacios¹,

Ushiña²,

Carrera³

2021

International Journal on Advanced Science, Engineering and Information Technology

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Section: B Radiometric and Atmospheric Correctionmentioning

confidence: 99%

Section: Minimum Noise Fraction (Mnf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pixel Purity Index Applied to the Mapping of Degraded Soils by the Presence of Cangahuas in the Ilaló Volcano, Ecuador

Palacios¹,

Ushiña²,

Carrera³

2021

International Journal on Advanced Science, Engineering and Information Technology

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“…This step is fundamental for the supervised classification of the image. The Pixel Purity Index (PPI) indicates the location of the purest pixels in the image [56][57][58][59]. By definition, a pure pixel is one that contains a single spectral material and therefore a single spectral signature.…”

Section: Pixel Purity Index (Ppi)mentioning

confidence: 99%

Geological and Mineralogical mapping in Moroccan central Jebilet using multispectral and hyperspectral satellite data and Machine Learning

Chakouri¹

2020

IJATCSE

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The Central Jebilet Massif is one of the main Palaeozoic outcrops in Morocco. This massif is characterized by its arid climate, its significant mining potential and the absence of plant cover, which favors the use of spatial remote sensing for geological mapping and mineral prospecting in this site. The objective of this study is the comparison of hyperspectral data from the Hyperion sensor of the Earth Observing-1 (EO-1) satellite and multispectral data from the Operational Land Imager (OLI) sensor of Landsat 8 in the discrimination of geological units and detection of iron caps in the study area. The classification by the Support Vector Machine (SVM) method allowed for a good mapping of the lithological units in the study area. The accuracy of the SVM classification of hyperspectral data is higher than that of multispectral data, which was demonstrated by the confusion matrix, notably an overall accuracy of 93.05% and 89.24%, respectively, and a kappa coefficient of 91.25% and 84.36%, respectively. Concerning the iron detection, the band rationing using both sensors have demonstrated a performance of detecting areas that contain more iron ores, especially, the iron caps of Kettara mine, with a small advantage of hyperspectral data. In overall, our results highlight the efficiency of machine learning classifier and hyperspectral data for the detection of iron ores and the discrimination of lithological units in arid regions. The use of hyperspectral and multispectral images has been shown to be a good technique for the characterization of iron deposits and lithological units, which may help in in mineral exploration engineering with reduced fieldwork and geochemistry.

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“…Note that IS and vegetation pixels with BSs belong to low-albedo and vegetation categories, respectively, according to previous research [60]. The endmember extraction of four categories of four categories was performed based on the combination of Minimum Noise Fraction Rotation (MNF) and Pixel Purity Index (PPI) methods [61].…”

Section: Extraction Of Pure Is and Vegetation Pixelsmentioning

confidence: 99%

Study of the Seasonal Effect of Building Shadows on Urban Land Surface Temperatures Based on Remote Sensing Data

Chen

Wang

et al. 2019

Remote Sensing

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Building shadows (BSs) frequently occur in urban areas, and their area and distribution display strong seasonal variations that significantly influence the urban land surface temperature (LST). However, it remains unclear how BSs affect the LST at the city scale because it is difficult to extract the shaded area at the subpixel scale and to connect such areas with the LST at the pixel scale. In this study, we combined the sun angle, building height, building footprint and building occlusion to extract the seasonal spatial distribution of BSs in the central area of Beijing. The effect of BSs on the LST was analyzed using LST retrieved from Landsat-8 thermal infrared sensor data. First, the relationship between the LST patch fragmentation and proportion of BSs in the sample areas was modeled without vegetation. Then, we quantitatively studied the mitigated intensity of the LST in pure impervious surfaces (IS) and vegetation pixels covered by BSs; next, we analyzed the LST sensitivity of these pixels to BSs. The results showed that the existence of BSs influences the fragmentation of the low LST patches strongly from summer to winter. On the other hand, pure IS pixels totally covered by BSs experienced a greater cooling effect, with 3.16 K on 10 July, and the lowest cooling occurred between 14 and 25 December, with a mean of 1.24 K. Without considering the relationship in winter, the LST is nonlinearly correlated to the building shadows ratio (BSR) in pixels, and an approximate 10% increase in the BSR resulted in decreases in the LST of approximately 0.33 K (mean of 16 April and 10 May), 0.37 K (10 July) and 0.24 K (28 September) for pure IS pixels, and 0.18 K, 0.20 K and 0.15 K, respectively, for pure vegetation pixels. Further analysis indicates that the LST of pure IS pixels is more sensitive to BSs than that of vegetation because the self-regulation mechanism of vegetation reduces the cooling effect of BSs. These findings can help urban planners understand the cooling characteristics of BSs and design suitable urban forms to resist urban heat islands (UHIs).

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Improved pure pixel identification algorithms to determine the endmembers in hyperspectral images

Cited by 5 publications

References 22 publications

Pixel Purity Index Applied to the Mapping of Degraded Soils by the Presence of Cangahuas in the Ilaló Volcano, Ecuador

Pixel Purity Index Applied to the Mapping of Degraded Soils by the Presence of Cangahuas in the Ilaló Volcano, Ecuador

Geological and Mineralogical mapping in Moroccan central Jebilet using multispectral and hyperspectral satellite data and Machine Learning

Study of the Seasonal Effect of Building Shadows on Urban Land Surface Temperatures Based on Remote Sensing Data

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