{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T23:51:01Z","timestamp":1717458661763},"reference-count":103,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2014,4,1]],"date-time":"2014-04-01T00:00:00Z","timestamp":1396310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Speckle noise (salt and pepper) is inherent to synthetic aperture radar (SAR), which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA), a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR) images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS) Phased-Array Synthetic-Aperture Radar (PALSAR) sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rond\u00f4nia State (municipality of Candeias do Jamari), containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by noise-reduction methods, such as maximum noise fraction (MNF) or noise-adjusted principal components (NAPCs). Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps.<\/jats:p>","DOI":"10.3390\/rs6042989","type":"journal-article","created":{"date-parts":[[2014,4,1]],"date-time":"2014-04-01T15:07:06Z","timestamp":1396364826000},"page":"2989-3019","source":"Crossref","is-referenced-by-count":1,"title":["Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0346-1684","authenticated-orcid":false,"given":"Osmar","family":"De Carvalho J\u00fanior","sequence":"first","affiliation":[{"name":"Departamento de Geografia, Campus Universit\u00e1rio Darcy Ribeiro, Universidade de Bras\u00edlia (UnB), Asa Norte, Bras\u00edlia, DF 70910-900, Brazil"}]},{"given":"Luz","family":"Maciel","sequence":"additional","affiliation":[{"name":"Instituto Nacional de Coloniza\u00e7\u00e3o e Reforma Agr\u00e1ria, Coordena\u00e7\u00e3o-Geral de Meio Ambiente DTM, SBN Quadra 1, bloco D, Ed. Pal\u00e1cio do Desenvolvimento, Bras\u00edlia, DF 71670-020, Brazil"}]},{"given":"Ana","family":"De Carvalho","sequence":"additional","affiliation":[{"name":"Instituto Nacional de Coloniza\u00e7\u00e3o e Reforma Agr\u00e1ria, Coordena\u00e7\u00e3o-Geral de Meio Ambiente DTM, SBN Quadra 1, bloco D, Ed. Pal\u00e1cio do Desenvolvimento, Bras\u00edlia, DF 71670-020, Brazil"}]},{"given":"Renato","family":"Guimar\u00e3es","sequence":"additional","affiliation":[{"name":"Departamento de Geografia, Campus Universit\u00e1rio Darcy Ribeiro, Universidade de Bras\u00edlia (UnB), Asa Norte, Bras\u00edlia, DF 70910-900, Brazil"}]},{"given":"Cristiano","family":"Silva","sequence":"additional","affiliation":[{"name":"Departamento de Geografia, Campus Universit\u00e1rio Darcy Ribeiro, Universidade de Bras\u00edlia (UnB), Asa Norte, Bras\u00edlia, DF 70910-900, Brazil"}]},{"given":"Roberto","family":"Gomes","sequence":"additional","affiliation":[{"name":"Departamento de Geografia, Campus Universit\u00e1rio Darcy Ribeiro, Universidade de Bras\u00edlia (UnB), Asa Norte, Bras\u00edlia, DF 70910-900, Brazil"}]},{"given":"Nilton","family":"Silva","sequence":"additional","affiliation":[{"name":"Faculdade de Engenharias do Gama, Universidade de Bras\u00edlia (UnB), Gama, DF 72444-240, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2014,4,1]]},"reference":[{"key":"ref_1","unstructured":"Goodenough, D.G., Guindon, B., and Teillet, P.M. (1979, January 23\u201327). Correction of Synthetic Aperture Radar and Multispectral Scanner Data Sets. . Ann Arbor, MI, USA."},{"key":"ref_2","unstructured":"Henninger, D.L., and Carney, J.H. (September, January 31). Shuttle Imaging Radar-A (SIR-A) Data as a Complement to Landsat Multispectral Scanner (MSS) Data. San Francisco, CA, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/0734-189X(83)90047-6","article-title":"Digital image smoothing and the sigma filter","volume":"24","author":"Lee","year":"1983","journal-title":"Comput. Vis. Graph. Image Process"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TSMC.1983.6313036","article-title":"A simple speckle smoothing algorithm for synthetic aperture radar images","volume":"13","author":"Lee","year":"1983","journal-title":"IEEE Trans. Syst. Man Cybern"},{"key":"ref_5","first-page":"2002","article-title":"Improved sigma filter for speckle filtering of SAR imagery","volume":"47","author":"Lee","year":"2009","journal-title":"IEEE Trans. Geosci. Remote. Sens"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TPAMI.1980.4766994","article-title":"Digital image enhancement and noise filtering by use of local statistics","volume":"2","author":"Lee","year":"1980","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TPAMI.1985.4767641","article-title":"Adaptive noise filtering for images with signal dependent noise","volume":"7","author":"Kuan","year":"1985","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"775","DOI":"10.3390\/s100100775","article-title":"Statistical modeling of SAR images: A survey","volume":"10","author":"Gao","year":"2010","journal-title":"Sensor"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1109\/36.536539","article-title":"Supervised classification of K-distributed SAR images of natural targets and probability of error estimation","volume":"34","author":"Nezry","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_10","unstructured":"George, S.F. (1968). The Detection of Nonfluctuating Targets in Log-Normal Clutter, Naval Research Laboratory. NRL Report 6796;."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1109\/TGRS.1986.289643","article-title":"Textural information in SAR images","volume":"24","author":"Ulaby","year":"1986","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_12","unstructured":"Tison, C., Nicolas, J.M., and Tupin, F. (2003, January 21\u201325). Accuracy of Fisher Distributions and Log-Moment Estimation to Describe Histograms of High-Resolution SAR Images over Urban Areas. Toulouse, France."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/TGRS.2004.834630","article-title":"A new statistical model for Markovian classification of urban areas in high-resolution SAR images","volume":"42","author":"Tison","year":"2004","journal-title":"IEEE Trans. Geosci. Romote Sens"},{"key":"ref_14","unstructured":"Oliver, C., and Quegan, S. (1998). Understanding Synthetic Aperture Radar Images, Artech House."},{"key":"ref_15","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification, Wiley. [2nd ed.]."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TGRS.2005.859349","article-title":"Dictionary-based stochastic expectation maximization for SAR amplitude probability density function estimation","volume":"44","author":"Moser","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_17","first-page":"267","article-title":"On estimation of probability density function and mode","volume":"33","author":"Parzen","year":"1962","journal-title":"Signal Process"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1109\/TGRS.2004.826821","article-title":"An advanced system for the automatic classification of multitemporal SAR images","volume":"42","author":"Bruzzone","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/TGRS.2004.842022","article-title":"Partially supervised classification of remote sensing images through SVM-based probability density estimation","volume":"43","author":"Mantero","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/LGRS.2010.2053517","article-title":"Enhanced dictionary-based SAR amplitude distribution estimation and its validation with very high-resolution data","volume":"8","author":"Krylov","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The Spectral Image Processing System (SIPS)\u2014Interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ"},{"key":"ref_22","unstructured":"De Carvalho J\u00fanior, O.A., and Meneses, P.R. (2000, January 23\u201325). Spectral Correlation Mapper (SCM): An Improving Spectral Angle Mapper. Pasadena, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1590\/S0074-02761993000400002","article-title":"Studies on the sandfly fauna of Samuel ecological station Porto Velho municipality, Rond\u00f4nia State, Brazil","volume":"88","author":"Azevedo","year":"1993","journal-title":"Mem. Inst. Oswaldo Cruz"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1590\/S0074-02761996000400008","article-title":"Forest Culicinae mosquitoes in the environs of Samuel hydroeletric plant, State of Rond\u00f4nia, Brazil","volume":"91","author":"Luz","year":"1996","journal-title":"Mem. Inst. Oswaldo Cruz"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1126\/science.260.5116.1905","article-title":"Evidence for tropical deforestation, fragmented habitat, and adversely affected habitat in the Brazilian Amazon: 1978\u20131988","volume":"260","author":"Skole","year":"1993","journal-title":"Science"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1046\/j.1523-1739.2002.01025.x","article-title":"Ecosystem decay of Amazonian Forest fragments: A 22-year investigation","volume":"16","author":"Laurance","year":"2002","journal-title":"Conserv. Biol"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3307","DOI":"10.1109\/TGRS.2007.901027","article-title":"ALOS PALSAR. A pathfinder mission for global-scale monitoring of the environment","volume":"45","author":"Rosenqvist","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1080\/01431160902777175","article-title":"Using dual polarized ALOS PALSAR data for detecting new fronts of deforestation in the Brazilian Amaz\u00f4nia","volume":"30","author":"Shimabukuro","year":"2009","journal-title":"Int. J. Remote Sens"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1109\/JSTARS.2010.2076398","article-title":"Large-area classification and mapping of forest and land cover in the Brazilian Amazon: A comparative analysis of ALOS\/PALSAR and Landsat data sources","volume":"3","author":"Walker","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0034-4257(98)00064-9","article-title":"Imaging spectroscopy and the Airborne Visible\/Infrared Imaging Spectrometer (AVIRIS)","volume":"65","author":"Green","year":"1998","journal-title":"Remote Sens. Environ"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A transformation for ordering multispectral data in terms of images quality with implications for noise removal","volume":"6","author":"Green","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/36.54356","article-title":"Enhancement of high spectral resolution remote sensing data by a noise\u2014Adjusted principal components transform","volume":"28","author":"Lee","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1071\/EG998324","article-title":"Noise reduction of aerial gamma-ray survey","volume":"29","author":"Dickson","year":"1988","journal-title":"Explor. Geophys"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1071\/EG00073","article-title":"Maximum noise fraction method reveals detail in aerial gamma-ray surveys","volume":"31","author":"Dickson","year":"2000","journal-title":"Explor. Geophys"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1590\/S0102-261X2006000300002","article-title":"Identifica\u00e7\u00e3o regional da floresta decidual na bacia do rio Paran\u00e3 a partir da an\u00e1lise multitemporal de imagens MODIS","volume":"24","author":"Hermuche","year":"2006","journal-title":"Rev. Bras. Geof\u00eds"},{"key":"ref_36","first-page":"1","article-title":"Classifica\u00e7\u00e3o de padr\u00f5es de savana usando assinaturas temporais NDVI do sensor MODIS no Parque Nacional Chapada dos Veadeiros","volume":"26","author":"Sampaio","year":"2008","journal-title":"Rev. Bras. Geof\u00eds"},{"key":"ref_37","first-page":"147","article-title":"Combining noise-adjusted principal components transform and median filter techniques for denoising MODIS temporal signatures","volume":"30","author":"Silva","year":"2012","journal-title":"Rev. Bras. Geof\u00eds"},{"key":"ref_38","unstructured":"Switzer, P., and Green, A.A. (1984). Min\/Max Autocorrelation Factors for Multivariate Spatial Imaging, Stanford University. Technical Report No. 6."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(95)00177-8","article-title":"A method for manual endmember selection and spectral unmixing","volume":"55","author":"Bateson","year":"1996","journal-title":"Remote Sens. Environ"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1109\/36.841987","article-title":"Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis","volume":"38","author":"Bateson","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2004.835299","article-title":"ICE: A statistical approach to identifying endmembers in hyperspectral images","volume":"42","author":"Berman","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/S0034-4257(96)00122-8","article-title":"Optimization of endmembers mixture analysis for spectral","volume":"59","author":"Tompkins","year":"1997","journal-title":"Remote Sens. Environ"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Winter, M. (1999, January 27). N-FINDR: An Algorithm for Fast Autonomous Spectral End-Member Determination in Hyperspectral Data. Denver, CO, USA.","DOI":"10.1117\/12.366289"},{"key":"ref_44","unstructured":"Boardman, J.W., and Kruse, F.A. (1994, January 9\u201312). Automated Spectral Analysis: A Geologic Example Using AVIRIS Data, North Grapevine Mountains, Nevada. San Antonio, TX, USA."},{"key":"ref_45","unstructured":"ENVI (2000). The Environment for Visualizing Images Users Guide, Research Systems Inc.. [4th ed.]."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Adams, J.B., and Gillespie, A.R. (2006). Remote Sensing of Landscapes with Spectral Images. A Physical Modeling Approach, Cambridge University Press.","DOI":"10.1017\/CBO9780511617195"},{"key":"ref_47","unstructured":"Jensen, J.R. (1986). Introductory Digital Image Processing, Prentice-Hall."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Congalton, R., and Green, K. (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC\/Lewis Press.","DOI":"10.1201\/9781420048568"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1113\/jphysiol.1968.sp008574","article-title":"Application of Fourier analysis to the visibility of gratings","volume":"197","author":"Campbell","year":"1968","journal-title":"J. Physiol"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1016\/0031-3203(91)90143-S","article-title":"Unsupervised texture segmentation using Gabor filters","volume":"24","author":"Jain","year":"1991","journal-title":"Pattern Recognit"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man-Cybern"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approaches to texture","volume":"67","author":"Haralick","year":"1979","journal-title":"Proc. IEEE"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3844\/ajassp.2011.26.32","article-title":"Gray-level co-occurrence matrix bone fracture detection","volume":"8","author":"Chai","year":"2011","journal-title":"Am. J. Appl. Sci"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.ijpharm.2013.10.024","article-title":"In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM)","volume":"460","author":"Ou","year":"2014","journal-title":"Int. J. Pharm"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TITB.2011.2119322","article-title":"Automated diagnosis of glaucoma using texture and higher order spectra features","volume":"15","author":"Dua","year":"2011","journal-title":"Trans. Inf. Technol. Biomed"},{"key":"ref_56","first-page":"43","article-title":"Automated vision system for recognising Lycra Spandex Defects","volume":"19","author":"Su","year":"2011","journal-title":"Fibres Text. East. Eur"},{"key":"ref_57","first-page":"466","article-title":"An identification system for classifying nonwoven quality","volume":"241","author":"Wang","year":"2013","journal-title":"Appl. Mech. Mater"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.compind.2013.12.001","article-title":"A sequential machine vision procedure for assessing paper impurities","volume":"65","author":"Bianconi","year":"2014","journal-title":"Comput. Ind"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1080\/01431160701730128","article-title":"Radar image texture as a function of forest stand age","volume":"29","author":"Champion","year":"2008","journal-title":"Int. J. Remote Sens"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/BF02826887","article-title":"SAR image classification based on its texture features","volume":"6","author":"Li","year":"2003","journal-title":"Geo-Spat. Inf. Sci"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.1016\/j.patrec.2012.05.018","article-title":"Target detection of ISAR data by principal component transform on co-occurrence matrix","volume":"33","author":"Gupta","year":"2012","journal-title":"Pattern Recog. Lett"},{"key":"ref_62","first-page":"385","article-title":"SAR sea ice discrimination using texture statistics: A multivariate approach","volume":"57","author":"Barber","year":"1991","journal-title":"Photogramm. Eng. Remote Sens"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1080\/01431160600557572","article-title":"Discriminating urban environments using multiscale texture and multiple SAR images","volume":"27","author":"Gamba","year":"2006","journal-title":"Int. J. Remote Sens"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TGRS.1984.350602","article-title":"Textural analysis and real-time classification of sea-ice types using digital SAR data","volume":"22","author":"Holmes","year":"1984","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"10625","DOI":"10.1029\/91JC00693","article-title":"Evaluation of second-order texture parameters for sea ice classification from radar images","volume":"96","author":"Shokr","year":"1991","journal-title":"J. Geophys. Res"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/36.752194","article-title":"Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices","volume":"37","author":"Soh","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TSMC.1976.5408777","article-title":"A comparative study of texture measures for terrain classification","volume":"6","author":"Weszka","year":"1976","journal-title":"IEEE Trans. Syst. Man-Cybern"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5589\/m02-004","article-title":"An analysis of co-occurrence texture statistics as a function of grey level quantization","volume":"28","author":"Clausi","year":"2002","journal-title":"Can. J. Remote Sens"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/TGRS.2003.817218","article-title":"Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery","volume":"42","author":"Clausi","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1080\/014311600209751","article-title":"A texture-based classification method for classifying built areas according to their density","volume":"21","author":"Karathanassi","year":"2000","journal-title":"Int. J. Remote Sens"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4137","DOI":"10.1080\/0143116031000070445","article-title":"Study of urban spatial patterns from SPOT panchromatic imagery using textural analysis","volume":"24","author":"Zhang","year":"2003","journal-title":"Int. J. Remote Sens"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/0034-4257(94)90047-7","article-title":"Remote sensing and the measurement of geographical entities. 2. The optimal spatial resolution","volume":"49","author":"Marceau","year":"1994","journal-title":"Remote Sens. Environ"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1080\/01431160512331316838","article-title":"The utility of texture analysis to improve perpixel classification for high to very high spatial resolution imagery","volume":"26","author":"Puissant","year":"2005","journal-title":"Int. J. Remote Sens"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1080\/01431160120769","article-title":"Texture analysis of IKONOS panchromatic data for Douglas-fire forest age class separability in British Columbia","volume":"22","author":"Franklin","year":"2001","journal-title":"Int. J. Remote Sens"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1080\/01431160010026005","article-title":"Texture analysis and data fusion in the extraction of topographic objects from satellite imagery","volume":"23","author":"Kiema","year":"2002","journal-title":"Int. J. Remote Sens"},{"key":"ref_76","unstructured":"Anvs, H., Bannari, A., He, D.C., and Morin, D. (1994, January 11\u201315). Texture Analysis for the Mapping of Urban Areas Using Airborne MEIS-ll Images. Strasbourg, France."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/TGRS.1995.8746010","article-title":"An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters","volume":"33","author":"Baraldi","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1080\/07055900.2001.9649675","article-title":"Comparison and fusion of co-occurrence, Gabor and MRF texture features for classification of SAR sea-ice imagery","volume":"39","author":"Clausi","year":"2001","journal-title":"Atmosphere-Ocean"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"3395","DOI":"10.1080\/01431169208904130","article-title":"Texture classification in aerial photographs and satellite data","volume":"13","author":"Sali","year":"1992","journal-title":"Int. J. Remote Sens"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/S0098-3004(01)00108-X","article-title":"Rapid extraction of image texture by co-occurrence using a hybrid data structure","volume":"28","author":"Clausi","year":"2002","journal-title":"Comput. Geosci"},{"key":"ref_81","unstructured":"Wei, L., Hu, Z., Guo, M., Jiang, M., and Zhang, S. (2012, January 15\u201317). Texture Feature Analysis in Oil Spill Monitoring by SAR Image. Hong Kong, China."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1080\/01431160701469016","article-title":"Textural and local spatial statistics for the object oriented classification of urban areas using high resolution imagery","volume":"29","author":"Su","year":"2008","journal-title":"Int. J. Remote Sens"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1016\/j.patcog.2010.10.017","article-title":"Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count","volume":"44","author":"Perez","year":"2011","journal-title":"Pattern Recognit"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.patcog.2004.08.004","article-title":"Gabor filters-based feature extraction for character recognition","volume":"38","author":"Wang","year":"2005","journal-title":"Pattern Recognit"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/TIP.2002.804262","article-title":"Comparison of texture features based on Gabor filters","volume":"11","author":"Grigorescu","year":"2002","journal-title":"IEEE Trans. Image Process"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s004220050323","article-title":"Computational models of visual neurons specialized in the detection of periodic and aperiodic oriented visual stimuli: Bar and grating cells","volume":"76","author":"Petkov","year":"1997","journal-title":"Biol. Cybern"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/0167-739X(95)00015-K","article-title":"Biologically motivated computationally intensive approaches to image pattern recognition","volume":"11","author":"Petkov","year":"1995","journal-title":"Future Gen. Comput. Syst"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/0042-6989(82)90113-4","article-title":"Spatial frequency selectivity of cells in macaque visual cortex","volume":"22","author":"Albrecht","year":"1982","journal-title":"Vis. Res"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1016\/j.patcog.2007.04.023","article-title":"Evaluation of the effects of Gabor filter parameters on texture classification","volume":"40","author":"Bianconi","year":"2007","journal-title":"Pattern Recognit"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/28.993164","article-title":"Defect detection in textured materials using Gabor filters","volume":"38","author":"Kumar","year":"2002","journal-title":"IEEE Trans. Ind. Appl"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TIP.2005.849319","article-title":"Design-based texture feature fusion using Gabor filters and co-occurrence probabilities","volume":"14","author":"Clausi","year":"2005","journal-title":"IEEE Trans. Image Process"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/S0031-3203(96)00068-4","article-title":"Object detection using Gabor filters","volume":"30","author":"Jain","year":"1997","journal-title":"Pattern Recognit"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.patrec.2004.09.013","article-title":"Comparison and fusion of multiresolution features for texture classification","volume":"26","author":"Li","year":"2004","journal-title":"Pattern Recognit. Lett"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1109\/34.531803","article-title":"Texture features for browsing and retrieval of image data","volume":"18","author":"Manjunath","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/BF00341922","article-title":"Texture discrimination by Gabor functions","volume":"55","author":"Turner","year":"1986","journal-title":"Biol. Cybern"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1016\/S0031-3203(99)00181-8","article-title":"Designing Gabor filters for optimal texture separability","volume":"33","author":"Clausi","year":"2000","journal-title":"Pattern Recognit"},{"key":"ref_97","first-page":"1266","article-title":"A complex-cell receptive-field model","volume":"53","author":"Spitzer","year":"1985","journal-title":"J. Neurosci"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1109\/TGRS.2008.918089","article-title":"Hyperspectral subspace identification","volume":"46","author":"Nascimento","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_99","first-page":"523","article-title":"Detection of bruises on apples using near-infrared hyperspectral imaging","volume":"46","author":"Lu","year":"2003","journal-title":"Trans.-Am. Soc. Agric. Eng"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/S0034-4257(03)00096-8","article-title":"Mapping nonnative plants using hyperspectral imagery","volume":"86","author":"Underwood","year":"2003","journal-title":"Remote Sens. Environ"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"425","DOI":"10.14358\/PERS.75.4.425","article-title":"Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf Coast","volume":"75","author":"Yang","year":"2009","journal-title":"Photogram. Eng. Remote Sens"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.jenvrad.2004.03.028","article-title":"Recent advances in aerial gamma-ray surveying","volume":"76","author":"Dickson","year":"2004","journal-title":"J. Environ. Radioact"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1190\/1.1598118","article-title":"Deconvolution and spatial resolution of airborne gamma-ray surveys","volume":"68","author":"Billings","year":"2003","journal-title":"Geophysics"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/4\/2989\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T04:48:11Z","timestamp":1717217291000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/4\/2989"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,4,1]]},"references-count":103,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2014,4]]}},"alternative-id":["rs6042989"],"URL":"https:\/\/doi.org\/10.3390\/rs6042989","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,4,1]]}}}