Textural analysis of IRS-1D panchromatic data for land cover classification

Rao, P. V. Narasimha; Sai, M. V. R. Sesha; Sreenivas, K.; Rao, Mala; Rao, B. R. M.; Dwivedi, R. S.; Venkataratnam, L.

doi:10.1080/01431160110104665

Cited by 62 publications

(32 citation statements)

References 9 publications

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“…Texture, which is a function of patterns in the spatial variation of pixel values in the imagery, is an important type of spatial information. Adding texture information to spectral information could improve classification accuracy (Franklin et al 2000;Rao et al 2002;Coburn and Roberts 2004;Zhang et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Influence of using texture information in remote sensed data on the accuracy of forest type classification at different levels of spatial resolution

Ota

Mizoue

Yoshida

2011

Journal of Forest Research

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We evaluated the influence of texture information from remote sensed data on the accuracy of forest type classification at different spatial resolutions. We used 4-m spatial resolution imagery to create five different sets of imagery with lower spatial resolutions down to 30 m. We classified forest type using spectral information alone, texture information alone, and spectral and texture information combined at each spatial resolution, and compared the classification accuracy at each resolution. The classification and regression tree method was used for classification. The accuracy of all three tests decreased slightly with lower spatial resolution. The accuracy with the combined data was generally higher than with either the spectral or texture information alone. At most resolutions, the lowest accuracy was with texture information alone. However, there was no clear difference in accuracy between the combined data and spectral data alone at 25-and 30-m spatial resolution. These results indicate that adding texture information to spatial information improves the accuracy of forest type classification from very high resolution (4-m spatial resolution) to medium resolution imagery (20-m spatial resolution), but this accuracy improvement does not appear to hold for relatively coarse resolution imagery (25-to 30-m spatial resolution).

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Section: Introductionmentioning

confidence: 99%

Influence of using texture information in remote sensed data on the accuracy of forest type classification at different levels of spatial resolution

Ota

Mizoue

Yoshida

2011

Journal of Forest Research

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“…Therefore, for all seven, most popular (Rao et al, 2002) textural features which were tested and applied in this contribution, a moving window of 3*3 pixels was used. These seven textural parameters are defined as follows: N is the number of pixels in a moving window, e.g.…”

Section: Grey-level Co-occurrence Matrix (Glcm)-based Texture Analysismentioning

confidence: 99%

“…Thus, many advanced classification approaches have been put forward in the general remote sensing literature, to improve the classification accuracy. For example, textural features, as proposed above, were used to improve the urban areas and land cover classification (Shaban and Dikshit, 2001;Rao et al, 2002;Chen et al, 2004); e.g. the artificial neural networks (ANN) was efficiently used in land cover classification (Kavzoglu and Mather, 2003); "fuzzy classification" was efficient in decreasing the mix-pixel problem (Shalan et al, 2003), and the knowledge-based system (KBS), especially, incorporating GIS, plays an important role because it is capable of managing different sources of data (Stefanov, 2001;Daniels, 2006;Lu and Weng, 2006;Alaaddin, 2008).…”

Section: Introductionmentioning

confidence: 99%

Textural and knowledge-based lithological classification of remote sensing data in Southwestern Prieska sub-basin, Transvaal Supergroup, South Africa

Li¹,

Frei

Altermann

2011

Journal of African Earth Sciences

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TM data and other medium spatial resolution satellite data are used in geological and lithologicalmineralogical classification on regular basis, although their usefulness is limited because of relatively coarse spectral resolution. In this contribution, we provide an example for the application of TM data for classification of rocks and minerals within the Neoarchean sedimentary and volcanic basin of Griqualand West, South Africa. An improved methodology is introduced that results in significantly higher classification accuracy. The TM multispectral image and Principal Component analysis (PCA) image of the test area were individually combined with textural features and then classified individually using a Maximum-Likelihood supervised classification (MLC). Subsequently, the two classified images were integrated, compared and re-classified in a knowledge-based system (KBS) using the generalized supplementary geological map on 1:250000 scale. With this method, the accuracy was improved from 54.3% to 83.2%, when compared to the former supervised classification.The method integrates the spectral and textural features, greatly contributing to the precision of the lithological classification, mapping and prospecting, in extensive areas where field work is limited by time and cost constrains.1

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“…The most relevant Grey Level Co-occurrence Matrix (GLCM) textural attributes according to the literature (Baraldi and Parmiggiani, 1995;Carr and de Miranda, 1998;Solberg, 1999;Pesaresi, 2000;Rao et al, 2002;Lu, 2005;Tuominen and Pekkarinen, 2005;Kayitakire et al, 2006), including Mean (ME), Variance (VAR), Standard Deviation (ST), Contrast (CON), Angular Second Moment (ASM), Entropy (ENT), Homogeneity (HOM), Energy (EN), Correlation (CO), Dissimilarity (DISS), and Maximum Probability (MP) have been calculated by MATLAB 7.9.0, for different spectral derivatives of SPOT-5 multispectral data including individual bands, band ratios and principle components (PCs), for four window sizes including 3×3 to 9×9, along with four different window orientations comprising of 0°, 45°, 90° and 135°. In addition to GLCM, sum and difference histogram (SADH) attributes proposed by Unser (1986) were calculated for the same window sizes.…”

Section: Attribute Extractionmentioning

confidence: 99%

Radar Backscatter and Optical Textural Indices Fusion for Pine Plantation Structure Mapping

Shamsoddini

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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One of the most important concerns of remote sensing research has been the quantification of forest structure variables. This issue has been investigated using different remotely sensed data including optical, radar and lidar data. Moreover, the utility of multisensor data has been examined for this task. Radar images are being considered as one of the available tools for forest studies; however, some constrains of this data such as speckle noise and the high sensitivity of this data to topography prevent it from being widely utilized for forest structure mapping. A possible solution is to integrate radar data with optical data to improve the accuracy of the biophysical parameter estimations; however, the results of the common fusion method does not show significant improvement if the efficacy of one of the datasets is much lower than the other one. In this study multi-date ALOS/PALSAR data with HH and HV polarizations were used along with SPOT-5 textural indices derived from the grey level co-occurrence matrix (GLCM), and the subtract and sum histogram (SADH), calculated in different orientations and window sizes for retrieval of a Pinus radiata plantation at plot-level in NSW, Australia. In order to overcome the deficiency of the common fusion method, a new fusion method called ratio fusion is examined for fusion of radar and optical data. The results showed that the estimation of biophysical parameters including mean height, mean DBH, stand volume, basal area and stocking using SPOT-5 textural indices was more accurate than that derived using the backscatter data derived from multi-date ALOS/PALSAR images. Also, the accuracy of estimation of these forest structure parameters increases when the ratio of the SPOT-5 textural indices to the radar backscatter is used for this task.

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Textural analysis of IRS-1D panchromatic data for land cover classification

Cited by 62 publications

References 9 publications

Influence of using texture information in remote sensed data on the accuracy of forest type classification at different levels of spatial resolution

Influence of using texture information in remote sensed data on the accuracy of forest type classification at different levels of spatial resolution

Textural and knowledge-based lithological classification of remote sensing data in Southwestern Prieska sub-basin, Transvaal Supergroup, South Africa

Radar Backscatter and Optical Textural Indices Fusion for Pine Plantation Structure Mapping

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