Revealing the Anatomy of Cities through Spectral Mixture Analysis of Multispectral Satellite Imagery: A Case Study of the Greater Cairo Region, Egypt.

Rashed, Tarek; Weeks, John R.; Gadalla, M. Saad; Hill, Allan G.

doi:10.1080/10106040108542210

Cited by 118 publications

(92 citation statements)

References 20 publications

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“…O modelo VIS provou ser útil para a classificação, embora devido à complexidade da área em estudo a exactidão não tenha sida tão elevada como era inicialmente esperado. Rashed et al (2001) conduziram uma análise de mistura espectral em imagens multiespectrais IRS-1C com o intuito de descrever a anatomia da região do Grande Cairo (Egipto). Esta análise foi baseada em quatro membros finais (Vegetação, superfície Impermeável, Solo e Sombra) cujas imagens de probabilidade foram classificadas com recurso a uma árvore de decisão, tendo a exactidão da classificação sido melhor que as obtidas pelos classificadores de mínima distância e máxima verosimilhança.…”

Section: As Classificações De Uso E Ocupação Do Solo E O Modelo Visunclassified

Classificação de uso do solo urbano através da análise linear de mistura espectral com imagens de satélite

Rocha¹,

Tenedório²,

Estanqueiro³

et al. 2012

FIT

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Resumo -Os ambientes urbanos são heterogéneos, sendo útil simplificá-los em combinações de materiais básicos de uso e ocupação do solo a fim de permitir estudos quantitativos. O modelo VIS proposto por Ridd em 1995 é um modelo conceptual que permite simplificar ambientes urbanos através da combinação de três componentes básicos: vegetação (V), superfície impermeável (I) e solo (S). A maioria dos usos urbanos pode ser interpretada à luz de combinações destes três componentes básicos. A análise da composição do VIS permite revelar que a maioria das características urbanas tem a sua própria composição original destes três factores, a qual é difícil de observar através dos métodos de classificação de imagens de saté-lite que operam ao nível do pixel. Este trabalho examina as características das classes de uso e ocupação do solo urbano na Grande Área Metropolitana de Lisboa (GAML) utilizando técnicas de classificação baseadas no sub-pixel, nomeadamente a Análise de Mistura Espectral (AME), desenvolvendo um modelo conceptual para caracterizar os padrões de ocupação, sendo explorada e testada a aplicabilidade da AME para medir a composição física da morfologia urbana, através de imagens multiespectrais Landsat 7 ETM+ e SPOT 5 HRVIR, avaliando, ao mesmo tempo, o efeito que as diferenças de resolução espacial podem ter nos resultados obtidos. Palavras-chave:Detecção remota, Análise de Mistura Espectral, uso do solo urbano.Abstract -URBAN LAND USE CLASSIFICATION THROUGH SATELLITE IMAGES SPEC-TRAL MIXTURE LINEAR ANALYSIS. Urban environments are heterogeneous by nature. Hence, to allow quantitative studies it's necessary to simplify them in combinations of basic land use/cover materials. The Ridd's VIS model (1995) is a conceptual representation that allows simplifying urban environments through the combination of three basic components: vegetation (V), impervious surface (I) and soil (S). The majority of urban uses can be interpreted as a combination of those three basic components. The VIS analysis allows to disclose that the mainstream of urban features has it's own VIS signature, which is difficult to detect through pixel-by-pixel based classifiers. This work examines the land use/cover characteristics of the Great Lisbon Metropolitan Area (GAML) using sub-pixel classification techniques, mainly linear spectral unmixing (LSU), developing a conceptual model to characterize the occupation standards. The LSU ability to measure the physical composition of urban morphology is also explored and tested. In this paper we use Landsat 7 ETM+ multispectral images and SPOT 5 HRVIR, evaluating at the same time the land use/cover signatures evolution and the effect of spatial resolution differences on the same signatures measurements.

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Section: As Classificações De Uso E Ocupação Do Solo E O Modelo Visunclassified

Classificação de uso do solo urbano através da análise linear de mistura espectral com imagens de satélite

Rocha¹,

Tenedório²,

Estanqueiro³

et al. 2012

FIT

View full text Add to dashboard Cite

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“…It is important to select only the variables that are most useful for separating land-cover or vegetation classes, especially when hyperspectral data are employed. Many approaches, such as principal component analysis, minimum noise fraction transform, discriminant analysis, 4 decision boundary feature extraction, non-parametric weighted feature extraction, wavelet transform, and spectral mixture analysis [11,[17][18][19][20][21][22][23] may be used for feature extraction, in order to reduce the data redundancy inherent in remotely sensed data or to extract specific land-cover information.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

A Survey of Remote Sensing Image Classification Approaches

2015

Int. J. Appl. Math. Mach. Learning

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Remote-sensing image classification is a complex process that may be affected via lots of factors. This paper mainly examines current practices, problems, and prospects of remotesensing image classification. The aim is to focus on the summarization of major advanced classification methods and the techniques used for improving classification accuracy. This paper review suggests that designing a suitable image processing procedure is a prerequisite for a successful classification of remotely sensed data and the selection of a suitable classification accuracy. Neural network, decision tree classifier, and knowledgebased classification have increasingly become important methods for remote-sensing image classification. However, more research is needed to identify and reduce uncertainties in the image processing chain to improve classification accuracy.

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“…Following the procedure proposed by (Rashed et al, 2001), endmembers is representative homogeneous pixels from satellite images through visualizing spectral scatterplots of image band combinations. In this study, however, the fourth endmember, i.e., burned peat endmember was selected despite containing heterogeneous pixels.…”

Section: Pelalawan Fraction Modelmentioning

confidence: 99%

Spectral Mixture Analysis (SMA) of Landsat Imagery for Land Cover Change Study of Highly Degraded Peatland in Indonesia

Sakti

Tsuyuki

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: Tropical Peatland Degradation, Spectral Mixture Analysis, Endmember Analysis, Burned Peat Fraction, Pelalawan District. ABSTRACT:Indonesian peatland, one of the world's largest tropical peatlands, is facing immense anthropogenic pressures such as illegal logging, degradation and also peat fires, especially in fertile peatlands. However, there still is a lack of appropriate tools to assess peatland land cover change. By taking Pelalawan district located in Sumatra Island, this study determines number of land cover endmembers that can be detected and mapped using new generation of Landsat 8 OLI in order to develop highquality burned peat fraction images. Two different image transformations, i.e. Principle Component Analysis (PCA), Minimum Noise Fraction (MNF) and two different scatterplot analyses, i.e. global and local, were tested and their accuracy results were compared. Analysis of image dimensionality was reduced by using PCA. Pixel Purity Index (PPI), formed by using MNF, was used to identify pure pixel. Four endmembers consisting of two types of soil (peat soil and dry soil) and two types of vegetation (peat vegetation and dry vegetation) were identified according to the scatterplot and their associated interpretations were obtained from the Pelalawan Fraction model. The results showed that local scatterplot analysis without PPI masking can detect high accuracy burned peat endmember and reduces RMSE value of fraction image to improve classification accuracy.

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Revealing the Anatomy of Cities through Spectral Mixture Analysis of Multispectral Satellite Imagery: A Case Study of the Greater Cairo Region, Egypt.

Cited by 118 publications

References 20 publications

Classificação de uso do solo urbano através da análise linear de mistura espectral com imagens de satélite

Classificação de uso do solo urbano através da análise linear de mistura espectral com imagens de satélite

A Survey of Remote Sensing Image Classification Approaches

Spectral Mixture Analysis (SMA) of Landsat Imagery for Land Cover Change Study of Highly Degraded Peatland in Indonesia

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