International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.10, No.12, Dec. 2018
An Efficient Texture Feature Extraction Algorithm for High Resolution Land Cover Remote Sensing Image Classification
Full Text (PDF, 906KB), PP.21-28
Remote sensing image classification is very much essential for many socio, economic and environmental applications in the society. They aid in agriculture monitoring, urban planning, forest monitoring, etc. Classification of a remote sensing image is still a challenging problem because of its multifold problems. A new algorithm LCDFOSCA (Linear Contact Distribution First Order Statistics Classification Algorithm) is proposed in this paper to extract the texture features from a Color remote sensing image. This algorithm uses linear contact distributions, mathematical morphology, and first-order statistics to extract the texture features. Later k-means is used to cluster these feature vectors and then classify the image. This algorithm is implemented on NRSC ‘Tirupathi’ area 2.5m, 1m color images and on Google Earth images. The algorithm is evaluated with various measures like the dice coefficient, segmentation accuracy, etc and obtained promising results.
Cite This Paper
A.V. Kavitha, A. Srikrishna, Ch. Satyanarayana, " An Efficient Texture Feature Extraction Algorithm for High Resolution Land Cover Remote Sensing Image Classification", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.12, pp. 21-28, 2018.DOI: 10.5815/ijigsp.2018.12.03
Minh-T Pham, S. Lefevre, E. Aptoula, Local feature-based attribute profiles for optical remote sensing image classification, IEEE Transactions on Geoscience and Remote Sensing 56 (2) (2017) 1199 - 1212.
J. Benediktsson, Pesaresi, Classification and feature extraction for remote sensing images from urban areas based on morphological transformations, IEEE Trans. on Geoscience and remote sensing 41 (9) (2003) 1942 - 1949.
O. Hagner and H. Reese, A method for calibrated maximum likelihood classification of forest types, Remote sensing of environment 110 (2007) 438-444.
L. Xue, X. Yang, et al, Building extraction of SAR images using morphological attribute profiles, Springer Communications signal processing, and systems 202 (2012) 13-21.
R. Li, F. Cao, Road network extraction from high-resolution remote sensing image using the homogeneous property and shape feature, Journal of Indian society of remote sensing 46 (1) (2018) 51-58.
G. R. Watmough, C. A. Palm, Clare Sullivan, An operational framework for object-based land use classification of heterogeneous rural landscapes, International Journal of Applied Earth Observation and Geoinformation 54 (4) (2017) 134-144. doi: org/10.1016/j.jag.2016.09.012.
F. Chen, R. Ren, T. V. de Voorde, Fast automatic airport detection in remote sensing images using convolutional neural networks, Remote Sens 10 (3) (2018) 443.
Y.M. Luo, Y. Ouyang, R.C. Zhang, H.M. Feng, Multi-feature joint sparse model for the classification of mangrove remote sensing images, International Journal of Geo-Information 6 (6) (2017) 177.
V. Dev, Y. Zang, M.Zhong, A review on image segmentation techniques with remote sensing perspective, ISPRS TC VII Symposium (2010) 31-42.
L. Ghouti, A. Bouridane, M. Ibrahim, S. Boussakta, Digital image watermarking using balanced multiwavelets, IEEE Trans on Signal Processing 54 (4) (2006) 1519-1536.
Y. Sun, G. jin He, Segmentation of high-resolution remote sensing image based on a marker-based watershed algorithm, IEEE conf procs at Shandong, China. doi:10.1109/FSKD.2008.249.
P.Mohanaiyah, P.satyanarayana, L.GuruKumar, Image texture feature extraction using glcm approach, International journal of scientific and research publications 3 (5) (2013) 1-5.
M. D. Mura, J. A. Benediktsson, et al., Morphological attribute profiles for the analysis of very high resolution images, IEEE trans on geoscience and remote sensing 48 (10) (2010) 3747-3762.
B. Song, J. Li, et al, Remotely sensed image classification using sparse representations of morphological attribute profiles, IEEE trans on geoscience and remote sensing 52 (8) (2014) 5123-5136.
B. Chaudhuri, B. Demir, S. Chaudhuri, L. Bruzzone, Multi-label remote sensing image retrieval using a semi-supervised graph-theoretic method, IEEE Transactions on Geoscience and Remote Sensing 56 (2) (2017) 1144-1158. doi: 10.1109/TGRS.2017.2760909.
M. Schroder, A. Dimai, Texture information in remote sensing images: A case study (1998).
Y. Deng, B. Manjunath, Unsupervised segmentation of color-texture regions in images and video, IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (2001) 800-810.
R. Harlick, K. Shanmugam, I. Dinstein, Texture features for image classi_cation, IEEE trans on systems, Man. and Cybernetics SMC-3 (1973) 610-621.
J. Serra, Image Analysis and Mathematical Morphology, Academic Press, New York, 1982.
J. Serra, P. Soille, Mathematical morphology and its applications to image processing, Springer science, and Bussiness media., 1984.
N. Robbe, T. Hengstermann, Remote sensing of marine oil spills from airborne platforms using multisensory systems, WIT Trans. on Ecology and the Environment 95 (2006) 9. doi:10.2495-WP060351.
S. Klemenjak, B. Waske, S. Valero, J. Chanussot, Automatic detection of rivers in high resolution SAR data, IEEE Journal of selected topics in applied earth observations and remote sensing 5 (2) (2012) 1-9.
P. Doraiswamy, et al, Crop classification in u.s. corn belt using modis imagery, IEEE Proceedings of Geoscience and Remote Sensing Symp 45 (4) (2007) 1074 - 1083.
D. Hug, G. Last, W. weil, A survey on contact distributions, LNP 600 (2002) 317-357.
M. B. Hansen, R. D. Gill, A. Baddeley, Kaplan-Meier type estimators for linear contact distributions. URL http://citeseerx.ist.psu.edu
P. Soille, M. Pesaresi, Advances in mathematical morphology applied to geoscience and remote sensing, IEEE Trans. on Geoscience and Remote sensing 40 (9) (2002) 2042-2055.
G. N. Srinivasan, G. Shobha, Statistical texture analysis, Proceedings of world academy of science, engineering and technology 36 (2008) 1264-1269.
I. Epifanio, P. Soille, Morphological texture features for unsupervised and supervised segmentation of natural landscapes, IEEE trans on Geoscience and Remote sensing 45 (4) (2007) 1074 - 1083.
N. OTSU, A threshold selection method from the gray level histogram, IEEE Trans. on Systems, Man, and Cybernetics 9 (1) (1979) 62-66.
L. Jianzhuang, e. a.Wenqing, L., Automatic thresholding of gray level pictures using two-dimensional Otsu method, IEEE proc on Circuits and systems doi:10.1109 CICCAS.1991.184351.
A.V.Kavitha, A.Srikrishna, Ch.Satyanarayana, Unsupervised linear contact distributions segmentation algorithm for land cover high resolution panchromatic images, Multimedia tools and applications (2018) 1-19 doi: https://doi.org/10.1007/s11042-018-6693-y.
I. Epifanio, G. Ayala, A random set view of texture classification, IEEE trans of image processing 11 (8) (2002) 859-867.
NRSC satellite images, https://bhuvan.nrsc.gov.in (2016).
Google earth, https://earth.google.com/download-earth.html (2017).
R. O. Duda, E. H. Peter, Pattern classification and scene analysis, 3rd Edition, Wiley, New York, 1973.
J. Fleiss, The measurement of interrater agreement. In: Statistical methods for rates and proportions, 2nd Edition, John Wiley and Sons, New York, 1981.
L. Dice, Measures of the amount of ecologic association between species, Ecology 26 (1945) 297-302. doi: 10.2307/1932409.
A. P. Zijdenbos, B. M. Dawant, R. Margolin, A. C. Palmer, Morphometric analysis of white matter lesions in MR images: method and validation, IEEE Trans Med Imaging 13 (1994) 716-724.