Abstract
With the development of remote sensing image applications, sparse-based representation classification approaches have been investigated for better classification accuracy. This paper introduces an improved classification method based on sparse representation by representing the test samples through a dictionary. The key components of our proposed method rely on the feature dictionary construction, sparse representation and image reconstruction. The dictionary is obtained by training samples according to their class for a sparse linear combination. The sparse representation for the image is expressed as sparse coefficients by solving an optimization problem. We describe the method of constructing a dictionary by computing a best matrix to represent all data vectors. We also describe the algorithm used to solve for the sparse representation. Finally, we discuss the way of using the sparse vector to reconstruct the image for classification. In the experiments, the proposed method is applied to two real high spatial resolution images for the classification in comparison to Backpropagation Neural Network, Support Vector Machine, Classification and Regression Trees and K-means. The experimental results show that the proposed method performs better than the benchmark methods in terms of classification accuracy.
Similar content being viewed by others
References
Aguera F, Aguilar JF, Aguilar AM (2008) Using texture analysis to improve perpixel classification of very high resolution images for mapping plastic greenhouses. ISPRS J Photogramm Remote Sens 63:635–646
Aharon M, Elad M, Bruckstein A (2006) The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans Sign Process 54:4311–4322
Benediktsson JA, Palmason JA, Sveinsson JR (2005) Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans Geosci Remote Sens 43:480–491
Bischof H, Schneider W, Pinz AJ (1992) Multispectral classification of Landsat-images using neural networks. IEEE Trans Geosci Remote Sens 30:482–490
Bruzzone L, Carlin L (2006) A multilevel context-based system for classification of very high spatial resolution images. IEEE Trans Geosci Remote Sens 44:2587–2600
Bruzzone L, Chi M, Marconcini M (2006) A novel transductive SVM for the semisupervised classification of remote sensing images. IEEE Trans Geosci Remote Sens 44:3363–3373
Chen S, Donoho D (1994) Basis pursuit. In: IEEE Conference Record of the Twenty-Eighth Asilomar Conference on Signals, Systems and Computers 1, 41–44
Chou PA (1991) Optimal partitioning for classification and regression trees. IEEE Trans Pattern Anal Mach Intell 4:340–354
Cotter SF, Rao BD, Engan K, Kreutz-Delgado K (2005) Sparse solutions to linear inverse problems with multiple measurement vectors. IEEE Trans Signal Process 53:2477–2488
Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52:1289–1306
Dópido I, Li J, Marpu PR, Plaza A (2013) Semisupervised self-learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 51:4032–4044
Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15:3736–3745
Fauvel M, Chanussot J, Benediktsson JA (2012) A spatial-spectral kernel-based approach for the classification of remote-sensing images. Pattern Recogn 45:381–392
Giacinto G, Roli F (2001) Design of effective neural network ensembles for image classification processes. Image Vis Comput 19:699–707
Goel PK, Prasher SO, Patel RM, Landry JA, Bonnell RB, Viau AA (2003) Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Comput Electron Agric 39:67–93
Heermann PD, Khazenie N (1992) Classification of multispectral remote sensing data using a back-propagation neural network. IEEE Trans Geosci Remote Sens 30:81–88
Huang X, Zhang L (2013) An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. Geosci Remote Sens IEEE Trans 51(1):257–272
Huang X, Zhang L, Li P (2007) Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery. IEEE Geosci Remote Sens Lett 4:260–264
Inglada J (2007) Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features. ISPRS J Photogramm Remote Sens 62:236–248
Jiang LH, Wang WS, Yang XR, Xie NF, Cheng YP (2011) Classification methods of remote sensing image based on decision tree technologies. Comput Comput Technol Agric 344:353–358
Li J, Zhang H, Zhang L (2015) Efficient superpixel-oriented multi-task joint sparse representation classification for hyperspectral imagery. IEEE Trans Geosci Remote Sens 53(10): 5338–5351
Li J, Zhang H, Zhang L, Ma L (2015) Hyperspectral anomaly detection by the use of background joint sparse representation. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):2523–2533
Luo M, Ma YF, Zhang HJ (1992) A spatial constrained k-means approach to image segmentation. In: Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on, 2, 738–742
Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41:3397–3415
Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42:1778–1790
Moody DI, Brumby SP, Rowland JC, Altmann GL (2014) Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries. J Appl Remote Sens 8(1):084793–084793
Moser G, Serpico SB, Benediktsson JA (2013) Land-cover mapping by Markov modeling of spatial-contextual information in veryhigh-resolution remote sensing images. Proc IEEE 101(3):631–651
Olshausen BA (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607–609
Ouma OY, Tateishi R (2008) Urban-trees extraction from QuickBird imagery using multiscale spectex-filtering and non-parametric classification. ISPRS J Photogramm Remote Sens 63:333–351
Palmason JA, Benediktsson JA, Sveinsson JR, Chanussot J (2005) Classification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis. In: Proc. 2005 Int. Conf. Geoscience and Remote Sensing Symp. (IGARSS), pp 25–29
Paola JD, Schowengerdt RA (1995) A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Trans Geosci Remote Sens 33(4):981–996
Pingel JT, Clarke CK, MaBride AW (2013) An improved simple morphological filter for the terrain classification of airborne LIDAR data. ISPRS J Photogramm Remote Sens 77:21–30
Rakotomamonjy A (2011) Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms. Signal Process 91:1505–1526
Ray S, Turi RH (1999) Determination of number of clusters in k-means clustering and application in colour image segmentation. In: Proceedings of the 4th international conference on advances in pattern recognition and digital techniques, pp 137–143
Reis S, Tasdemir K (2011) Identification of hazelnut fields using spectral and Gabor textural features. ISPRS J Photogramm Remote Sens 66:652–661
Rubinstein R, Bruckstein AM, Elad M (2010) Dictionaries for sparse representation modeling. Proc IEEE 98:1045–1057
Stoeva S, Nikov A (2000) A fuzzy backpropagation algorithm. Fuzzy Sets Syst 112(1):27–39
Tropp J, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. Inf Theory IEEE Trans 53(12):4655–4666
Tropp JA, Gilbert AC, Strauss MJ (2006) Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit. Signal Process Spec Issue Sparse Approximations Signal Image Process 86:572–588
Tropp JA, Wright SJ (2010) Computational methods for sparse solution of linear inverse problems. Proc IEEE 98:948–958
Tuia D, Jordi MM, Kanevski M, Camps G (2011) Structured output SVM for remote sensing image classification. J Sign Process Syst 65:301–310
Tuia D, Ratle F, Pozdnoukhov A, Camps-Valls G (2010) Multisource composite kernels for urban-image classification. IEEE Geosci Remote Sens Lett 7:88–92
Yang J, Su M, Yu P (2010) A novel K-nearest neighbor classifier based on adaptive metric formed by features extracted by nonparametric feature extraction mode. Int J Adv Inf Technol 4(2):89–103
Yu X, Cao T, Yang C, Chen H, Wu S (2009) Remote sensing image classification based on sparse component analysis. Prog Geophys 24:2274–2279
Yu XC, Dai S, Hu D, Jiang QY (2011) HHFNN based on lasso function and its application remote sensing image classification. Chin J Geophys 54(6):1672–1678
Zhang H, Li J (2014) A nonlocal weighted joint sparse representation classification method for hyperspectral imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 7:2056–2065
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 61163042 61503235 and 41272359), and funded by Key Discipline (Cartography and Geographic Information System of Hainan Normal University).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wu, S., Chen, H., Bai, Y. et al. A remote sensing image classification method based on sparse representation. Multimed Tools Appl 75, 12137–12154 (2016). https://doi.org/10.1007/s11042-016-3320-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-016-3320-7