Abstract
Images (HSIs) are popular in diversified applications, such as geosciences, biomedical imaging, molecular biology, agriculture, astronomy, food quality and safety assessment, surveillance and physics-related research. The rich spatial and spectral information of HSI is the key factors for robust representation of class-specific objects, in remote sensing applications. But, these images often suffer from Hughes effect. This effect causes the recording of information about a single scene in multiple spectral bands. This demands a dimensionality reduction step, which can either be a feature reduction/extraction or a feature selection. The feature selection process is commonly called band selection (BS) in the HS data set. The current study proposes an unsupervised BS technique, which is accomplished in three steps, including preprocessing of spectral bands, adjacent band clustering, and multi-agent optimization. Spatio-spectral (using a simple Gaussian filter) information is extracted to evaluate the performance using SVM classifier with different state-of-the-art band selection approaches. The performance of the proposed approach is evaluated for metrics including overall accuracy (OA), average accuracy (AA) and Kappa (\(\kappa\)). The experimental results are promising as these surpass that of other approaches.
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References
Zhang A, Sun G, Wang Z, Yao Y (2015) A hybrid genetic algorithm and gravitational search algorithm for global optimization. Neural Netw World 25(1):53–73
Raymer ML, Punch WF, Goodman ED, Kuhn LA, Jain AK (2000) Dimensionality reduction using genetic algorithms. IEEE Trans Evol Comput 4(2):164–171
Hughes G (1968) On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theory 14(1):55–63
Keshava N (2004) Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Trans Geosci Remote Sens 42(7):1552–1565
Ashokkumar L, Shanmugam S (2014) Hyperspectral band selection and classification of Hyperion image of Bhitarkanika mangrove ecosystem, eastern India. SPIE Remote Sens 9239(October 2014):923914
Chang C-I (2000) An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis. IEEE Trans Inf Theory 46(5):1927–1932
Fauvel M, Dechesne C, Zullo A, Ferraty F (2015) Fast forward feature selection of hyperspectral images for classification with Gaussian mixture models. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):2824–2831
Li S, Zheng Z, Wang Y, Chang C, Yu Y (2016) A new hyperspectral band selection and classification framework based on combining multiple classifiers. Pattern Recognit Lett 83:152–159
Yang C, Lee WS, Gader P (2014) Hyperspectral band selection for detecting different blueberry fruit maturity stages. Comput Electron Agric 109:23–31
Datta A, Ghosh S, Ghosh A (2014) Band elimination of hyperspectral imagery using partitioned band image correlation and capacitory discrimination. Int J Remote Sens 35(2):554–577
Ramzi P, Samadzadegan F, Reinartz P (2014) Classification of hyperspectral data using an AdaBoostSVM technique applied on band clusters. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2066–2079
Chang Y, Chen K (2011) A parallel simulated annealing approach to band selection for high-dimensional remote sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens 4(3):579–590
Medjahed SA, Saadi TA, Benyettou A, Ouali M (2016) A new post-classification and band selection frameworks for hyperspectral image classification. Egypt J Remote Sens Space Sci 19(2):163–173
Paul A, Bhattacharya S, Dutta D, Sharma JR, Dadhwal VK (2015) Band selection in hyperspectral imagery using spatial cluster mean and genetic algorithms. GI Sci Remote Sens 52(6):643–659
Hongjun S, Cai Y, Qian D (2017) Firefly-algorithm-inspired framework with band selection and extreme learning machine for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 10(1):309–320
Zhang M, Ma J, Gong M (2017) Unsupervised hyperspectral band selection by fuzzy clustering with particle swarm optimization. IEEE Geosci Remote Sens Lett 14(5):773–777
Patro RN (2019) Spectral clustering and spatial frobenius norm-based jaya optimisation for bs of hyperspectral images. IET Image Process 13(8):307–315
Wang M, Wan Y, Ye Z, Gao X, Lai X (2018) A band selection method for airborne hyperspectral image based on chaotic binary coded gravitational search algorithm. Neurocomputing 273:57–67
Zhang A, Sun G, Wang Z (2015) Optimized hyperspectral band selection using hybrid genetic algorithm and gravitational search algorithm. In: MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing, vol 9814. International Society for Optics and Photonics, p 981403
Ghamisi P, Benediktsson JA (2015) Feature selection based on hybridization of genetic algorithm and particle swarm optimization. IEEE Geosci Remote Sens Lett 12(2):309–313
Subudhi S, Patro RN, Biswal PK (2019) Pso-based synthetic minority oversampling technique for classification of reduced hyperspectral image. In: Bansal JC, Das KN, Nagar A, Deep K, Ojha AK (eds) Soft computing for problem solving. Springer, Singapore, pp 617–625
Ahmad R, Lee Y-C, Rahimi S, Gupta B (2007) A multi-agent based approach for particle swarm optimization. In: 2007 International Conference on Integration of Knowledge Intensive Multi-Agent Systems. IEEE, pp 267–271
Patro RN, Subudhi S, Biswal PK, Sahoo HK (2019) Probabilistic histogram-based band selection and its effect on classification of hyperspectral images. In: Bansal JC, Das KN, Nagar A, Deep K, Ojha AK (eds) Soft computing for problem solving. Springer, Singapore, pp 559–570
Pahlavani P, Hasanlou M, Nahr ST (2017) Band selection and dimension estimation for hyperspectral imagerya new approach based on invasive weed optimization. J Indian Soc Remote Sens 45(1):11–23
Patro RN, Subudhi S, Biswal PK, Dell’Acqua F (2019) Dictionary-based classifiers for exploiting feature sequence information and their application to hyperspectral remotely sensed data. Int J Remote Sens 0(0):1–29
Patro RN, Subudhi S, Biswal PK, DellAcqua F, Sahoo HK (2019) Conditional nearest regularized subspace classifiers: a fast classification approach for HSI. Int J Remote Sens 0(0):1–25
Rodarmel C, Shan J (2002) Principal component analysis for hyperspectral image classification. Surv Land Inf Sci 62(2):115–122
Sheet D, Garud H, Suveer A, Mahadevappa M, Chatterjee J (2010) Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans Consum Electron 56(4):2475–2480
Dell’Acqua F, Gamba P, Ferrari A, Palmason JA, Benediktsson JA, Arnason K (2004) Exploiting spectral and spatial information in hyperspectral urban data with high resolution. IEEE Geosci Remote Sens Lett 1(4):322–326
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Kalidindi, K.R., Gottumukkala, P.S.V. & Davuluri, R. Derivative-based band clustering and multi-agent PSO optimization for optimal band selection of hyper-spectral images. J Supercomput 76, 5873–5898 (2020). https://doi.org/10.1007/s11227-019-03058-3
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DOI: https://doi.org/10.1007/s11227-019-03058-3