Abstract
Automatic training data extraction approaches are new solutions for supervised classification. Most of the researches are aimed to develop automatic methods since conventional methods are time- and cost-consuming. Also, in conventional methods the results are impacted by the expertise of operators. On the other hand, factors such as size, number, distribution and homogeneity of training samples affect the overall accuracy. Therefore, examining the impact of these factors is time-consuming in traditional methods. Newly presented approaches have removed the problems of traditional methods although they have their own issues such as more detailed data in hyper-spectral images or heterogeneity inner different classes. These factors influence the accuracy of classification. In this paper, we study the behavior of raw data and extracted features separately, and then identify a stable pattern for automatic recognition of each class. The study is based on frequency distribution and statistical analysis of spectral behavior of ground features. Investigation on the results exposes that some classes have stable patterns and can easily be identified according to their behavior. The behavior of each class depends on selected bands and features, and can be regular or irregular. For instance, water class has low density distribution that makes it difficult to fit a distribution function, but it is distinguishable thorough unpredictable distribution function. In the other hand, urban class has regular pattern and an appropriate distribution can be fitted to the frequency of the features extracted from its sample dataset. Although an individual class may be recognized through several features simultaneously, using whole available features is time-consuming. So, just intrinsic combination of features should be used for classification. In the research, the behaviors of studied classes have been presented in different charts and diagrams, and ultimately, a new algorithm (in flowchart format) has been presented for intelligent sampling and training data extraction based on statistical analysis of spectral behavior of classes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbasi, B., Arefi, H., Bigdeli, B., & Roessner, S. (2015). Automatic generation of training data for hyperspectral image classification using support vector machine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences,40(7), 575.
Bruzzone, L., & Prieto, D. F. (2002). An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images. IEEE Transactions on Image Processing,11(4), 452–466.
Colditz, R. R., Schmidt, M., Ressl, R., Hansen, M. C. & Dech, S. (2008). A method for selecting training data and its effect on automated land cover mapping of large areas. In: Geoscience and remote sensing symposium, 2008. IGARSS 2008. IEEE International, IEEE.
Coles, S., Bawa, J., Trenner, L., & Dorazio, P. (2001). An introduction to statistical modeling of extreme values. Berlin: Springer.
Erikson, M. (2004). Species classification of individually segmented tree crowns in high-resolution aerial images using radiometric and morphologic image measures. Remote Sensing of Environment,91(3–4), 469–477.
Foody, G. M., & Mathur, A. (2004). Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment,93(1–2), 107–117.
Haara, A., & Haarala, M. (2002). Tree species classification using semi-automatic delineation of trees on aerial images. Scandinavian Journal of Forest Research,17(6), 556–565.
Jackson, Q., & Landgrebe, D. A. (2001). An adaptive classifier design for high-dimensional data analysis with a limited training data set. IEEE Transactions on Geoscience and Remote Sensing,39(12), 2664–2679.
Kavzoglu, T. (2009). Increasing the accuracy of neural network classification using refined training data. Environmental Modelling and Software,24(7), 850–858.
Kawulok, M., & Nalepa, J. (2014). Dynamically adaptive genetic algorithm to select training data for SVMs. In Ibero-American conference on artificial intelligence. Springer.
Mathur, A., & Foody, G. M. (2008). Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing,29(8), 2227–2240.
McCaffrey, T. M., & Franklin, S. E. (1993). Automated training site selection for large-area remote-sensing image analysis. Computers & Geosciences,19(10), 1413–1428.
Nalepa, J., & Kawulok, M. (2016). Adaptive memetic algorithm enhanced with data geometry analysis to select training data for SVMs. Neurocomputing,185, 113–132.
Ojaghi, S., Ahmadi, F. F., & Ebadi, H. (2016). A new method for semi-automatic classification of remotely sensed images developed based on the cognitive approaches for producing spatial data required in geomatics applications. Arabian Journal of Geosciences,9(19), 724.
Sibanda, M., Mutanga, O., & Rouget, M. (2017). Testing the capabilities of the new WorldView-3 space-borne sensor’s red-edge spectral band in discriminating and mapping complex grassland management treatments. International Journal of Remote Sensing,38(1), 1–22.
Waser, L., Baltsaviasb, E., Ginzler, C., & Küchler, M. (2010). Semiautomatic classification of tree species by means of multi-temporal airborne digital sensor data ADS40. Red,610, 660.
Waser, L. T., Ginzler, C., Kuechler, M., Baltsavias, E., & Hurni, L. (2011). Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data. Remote Sensing of Environment,115(1), 76–85.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Hoseynzadeh, S., Farnood Ahmadi, F. Developing a New Algorithm Based on Statistical Analysis of the Spectral Behavior of Features for Extracting Training Data Automatically for Classification of Remotely Sensed Images. J Indian Soc Remote Sens 48, 535–551 (2020). https://doi.org/10.1007/s12524-019-01099-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-019-01099-y