Evaluation of speckle filtering and texture analysis methods for land cover classification from SAR images

Nyoungui, A. Ndi; Tonyé, Emmanuel; Akono, Alain

doi:10.1080/01431160110036157

Cited by 79 publications

(29 citation statements)

References 49 publications

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“…Gray level co-occurrence matrix (GLCM) is a common method used in describing image texture by studying the spatial correlation characteristics of gray as well as the spatial distribution relationship between pixels, thus reflecting the spatial relationship between pixels and the characteristics of image elements (Davis et al, 1979;Clausi, 2002). GLCM is widely applied in the detection and classification of surface features in remote sensing images, especially in SAR and radar images (Rajesh et al, 2001;Nyoungui et al, 2002;Beauchemin et al, 1996;Shanmugan et al, 1981;Shokr, 1991), but less in optical images (Marceau et al, 1990;Gong et al, 1992).…”

Section: Introductionmentioning

confidence: 99%

Improving MODIS sea ice detectability using gray level co-occurrence matrix texture analysis method: A case study in the Bohai Sea

Su¹,

Wang²,

Xiao³

et al. 2013

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Improving MODIS sea ice detectability using gray level co-occurrence matrix texture analysis method: A case study in the Bohai Sea

Su¹,

Wang²,

Xiao³

et al. 2013

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Many texture measures have been developed (Haralick et al, 1973;He & Wang, 1990;Unser, 1995;Riou & Seyler, 1997). In previous research, texture measures were mainly used for LULC classification (Franklin & Peddle, 1989;Marceau et al, 1990;Augusteijn et al, 1995;Franklin et al, 2000;ndi Nyoungui et al, 2002;Podest & Saatchi, 2002). Of the many texture measures, the grey-level cooccurrence matrix (GLCM) may be the most common texture used for improving LULC classification (Marceau et al, 1990;Franklin et al, 2000;ndi Nyoungui et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

Batistella

2005

Acta Amaz.

149

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Many texture measures have been developed and used for improving land-cover classification accuracy, but rarely has research examined the role of textures in improving the performance of aboveground biomass estimations. The relationship between texture and biomass is poorly understood. This paper used Landsat Thematic Mapper (TM) data to explore relationships between TM image textures and aboveground biomass in Rondônia, Brazilian Amazon. Eight grey level co-occurrence matrix (GLCM) based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation), associated with seven different window sizes (5x5, 7x7, 9x9, 11x11, 15x15, 19x19, and 25x25), and five TM bands (TM 2, 3, 4, 5, and 7) were analyzed. Pearson's correlation coefficient was used to analyze texture and biomass relationships. This research indicates that most textures are weakly correlated with successional vegetation biomass, but some textures are significantly correlated with mature forest biomass. In contrast, TM spectral signatures are significantly correlated with successional vegetation biomass, but weakly correlated with mature forest biomass. Our findings imply that textures may be critical in improving mature forest biomass estimation, but relatively less important for successional vegetation biomass estimation. KEYWORDStexture, aboveground biomass, TM image, correlation, Amazon Explorando texturas de imagens TM e suas relações com estimativas de Biomassa em Rondônia. (i.e., média, variância, homogeneidade, contraste, dissimilaridade, entropia, segundo momento e correlação), associadas com sete diferentes tamanhos de janela (5x5, 7x7, 9x9, 11x11, 15x15, 19x19 e 25x25) e cinco bandas TM (TM 2, 3, 4, 5 RESUMO Muitas medidas de textura têm sido desenvolvidas e utilizadas para melhorar a acurácia de classificações de cobertura das terras, mas raramente têm-se avaliado a importância dessas medidas em estimativas de biomassa. Este trabalho utilizou dados Landsat TM para explorar as relações entre texturas de imagens TM e biomassa em Rondônia, Amazônia. Foram analisadas oito medidas de textura baseadas em matrizes de co-ocorrência de tons de cinza

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“…Speckle algorithms can produce satisfactory results when used properly (Nyoungui et al, 2002).The overall classification accuracy improvement using Gamma-MAP 27  27 was 25% higher compared to unfiltered radar data ( Table 2). The other filtering techniques, Median and Lee-sigma also improved the overall classification by 20% and 17% respectively.…”

Section: Comparison Among Different De-speckling Techniquesmentioning

confidence: 90%

“…This is due to the coherent nature of the radar wave (Jenson, 2005), which may create an artificial heterogeneity for a homogeneous region. Speckle affects image classification and interpretation (Nyoungui et al, 2002). Therefore, it is crucial to reduce speckle noise before radar data is used in classification studies (Maghsoudi et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Land Use Classification and Analysis Using Radar Data Mining in Ethiopia

Tadesse

Aguirre

et al. 2017

IJARSG

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Land use classification in tropical areas, is hindered by frequent cloud cover which limits the availability of optical satellite data. Satellite-borne radar is a possible alternative to optical data for land use classification in tropical areas. However, radar data is affected by noise (i.e., speckle) that must be minimized before its use in land classification. Median, Lee-Sigma, and Gamma-MAP de-speckling techniques were applied to Fine Beam, Dual polarization (FBD) PALSAR radar data acquired over central Ethiopia. Each of the de-speckled images were then subjected to supervised classification using Maximum Likelihood, C4.5, Multilayer Perceptron and Stacking techniques. Validation results indicated that de-speckling techniques improved classification accuracy by up to 25%, 20% and 16% using Gamma-MAP, Median and Lee-Sigma respectively. Gamma-MAP de-speckling in combination with the Multilayer perceptron classifier achieved the best overall classification accuracy at 91.2%. This study proved the importance of radar data as an alternative source of information for land use classification in the tropics. Further research should focus on the application of radar data for forest fire detection and crop classification. The use of fully polarized radar data has the potential to further improve the proposed land use classification in tropical countries.

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Evaluation of speckle filtering and texture analysis methods for land cover classification from SAR images

Cited by 79 publications

References 49 publications

Improving MODIS sea ice detectability using gray level co-occurrence matrix texture analysis method: A case study in the Bohai Sea

Improving MODIS sea ice detectability using gray level co-occurrence matrix texture analysis method: A case study in the Bohai Sea

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

Land Use Classification and Analysis Using Radar Data Mining in Ethiopia

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