Abstract
High spatial resolution (HSR) images are valuable data sources for urban applications. HSR images by high intra-class and low inter-class variabilities lead to a reduction in the statistical separability of the different land-cover classes in the spectral domain; therefore, conventional classification methods using merely spectral and textural information have proven to be inadequate for the HSR data. In this regard, interpretation systems by employing spatial and conceptual compatibility can be useful to closer a machining process to human analysis. In this research, a new framework has been introduced to classify remotely sensed imagery using a combination of ontological rules and geographic object-based image analysis. This paper to some degree attempts to untangle a few of the gaps in this field, especially by incorporating multi-scale analysis into the proposed framework, which is very advantageous in such an application. The present study used interactive segmentation and interpretation segmentation process with respect to the geometry of the image classes. In the proposed method, the segmentation process has been performed to avoid under-segmentation problems at several levels of the scale. The levels of scale are entered in the process of scoring and interpretation of the decision (not just applied at the level of results). This brings the process of labeling and interpretation closer to structural and natural reality. Additionally, a hybrid decision-making process (knowledge based and support vector machine) has been considered to achieve better results. The knowledge-based method is implemented to model the ontological relationships with the aim of labeling and controlling the decision-making process. To evaluate the efficiency of the method, the results of this research were assessed and compared with those of other methods in an urban area. The results showed that the proposed technique improved the overall accuracy and kappa coefficient by 9% and 11.5%, respectively.
Similar content being viewed by others
References
Almendros-Jiménez, J. M., Domene, L., & Piedra-Fernández, J. A. (2013). A framework for ocean satellite image classification based on ontologies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,6, 1048–1063.
Andrés, S., Arvor, D., Mougenot, I., Libourel, T., & Durieux, L. (2017). Ontology-based classification of remote sensing images using spectral rules. Computers and Geosciences,102, 158–166.
Andres, S., Arvor, D., & Pierkot, C. (2012). Towards an ontological approach for classifying remote sensing images. In 2012 8th international conference on signal image technology and internet based systems (SITIS) (pp. 825–832). New York: IEEE.
Arvor, D., Durieux, L., Andrés, S., & Laporte, M.-A. (2013). Advances in geographic object-based image analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing,82, 125–137.
Axelsson, P. (2000). DEM generation from laser scanner data using adaptive TIN models. International Archives of Photogrammetry and Remote Sensing,33, 111–118.
Ban, Y., Hu, H., & Rangel, I. M. (2010). Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: Object-based and knowledge-based approach. International Journal of Remote Sensing,31, 1391–1410.
Belgiu, M., & Drǎguţ, L. (2014). Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery. ISPRS Journal of Photogrammetry and Remote Sensing,96, 67–75.
Carletta, J. (1996). Assessing agreement on classification tasks: The kappa statistic. Computational Linguistics,22, 249–254.
Chandra, A., & Yao, X. (2006). Evolving hybrid ensembles of learning machines for better generalisation. Neurocomputing,69, 686–700.
Chowdary, B., & Radhika, Y. (2018). A survey on applications of data mining techniques. International Journal of Applied Engineering Research,13, 5384–5392.
Clement, V., Giraudon, G., Houzelle, S., & Sandakly, F. (1993). Interpretation of remotely sensed images in a context of multisensor fusion using a multispecialist architecture. Geoscience and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing,31, 779–791.
Conţiu, Ş., & Groza, A. (2016). Improving remote sensing crop classification by argumentation-based conflict resolution in ensemble learning. Expert Systems with Applications,64, 269–286.
Corcoran, J., Knight, J., Pelletier, K., Rampi, L., & Wang, Y. (2015). The effects of point or polygon based training data on RandomForest classification accuracy of wetlands. Remote Sensing,7, 4002–4025.
Costa, H., Foody, G. M., & Boyd, D. S. (2017). Using mixed objects in the training of object-based image classifications. Remote Sensing of Environment,190, 188–197.
de Leeuw, J., Jia, H., Yang, L., Liu, X., Schmidt, K., & Skidmore, A. (2006). Comparing accuracy assessments to infer superiority of image classification methods. International Journal of Remote Sensing,27, 223–232.
Drăguţ, L., Csillik, O., Eisank, C., & Tiede, D. (2014). Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing,88, 119–127.
Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests. International Journal of Remote Sensing,33, 4502–4526.
El-Dahshan, E.-S. A., Hosny, T., & Salem, A.-B. M. (2010). Hybrid intelligent techniques for MRI brain images classification. Digital Signal Processing,20, 433–441.
Forestier, G., Puissant, A., Wemmert, C., & Gançarski, P. (2012). Knowledge-based region labeling for remote sensing image interpretation. Computers, Environment and Urban Systems,36, 470–480.
Giacinto, G., Roli, F., & Bruzzone, L. (2000). Combination of neural and statistical algorithms for supervised classification of remote-sensing images. Pattern Recognition Letters,21, 385–397.
Gu, H., Li, H., Yan, L., Liu, Z., Blaschke, T., & Soergel, U. (2017). An object-based semantic classification method for high resolution remote sensing imagery using ontology. Remote Sensing,9, 329.
Hay, G. J., Castilla, G., Wulder, M. A., & Ruiz, J. R. (2005). An automated object-based approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observation and Geoinformation,7, 339–359.
Huang, M.-J., Shyue, S.-W., Lee, L.-H., & Kao, C.-C. (2008). A knowledge-based approach to urban feature classification using aerial imagery with lidar data. Photogrammetric Engineering and Remote Sensing,74, 1473–1485.
Khatami, R., Mountrakis, G., & Stehman, S. V. (2016). A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment,177, 89–100.
Khelifa, D., & Mimoun, M. (2012). Object-based image analysis and data mining for building ontology of informal urban settlements. In Image and signal processing for remote sensing XVIII. International society for optics and photonics (p. 85371I).
Kiani, A., Ebadi, H., Ahmadi, F. F., & Masoumi, S. (2014). Design and implementation of an expert interpreter system for intelligent acquisition of spatial data from aerial or remotely sensed images. Measurement,47, 676–685.
Kiani, A., Ebadi, H., & Farnood Ahmadi, F. (2019). Development of an object-based interpretive system based on weighted scoring method in a multi-scale manner. ISPRS International Journal of Geo-Information,8, 398.
Kohli, D., Sliuzas, R., Kerle, N., & Stein, A. (2012). An ontology of slums for image-based classification. Computers, Environment and Urban Systems,36, 154–163.
Kudrat, M., Sharma, K., Tiwari, A., Kumar, P., Prabhakaran, B., & Manchanda, M. (2000). Discrimination of newly planted and ratoon crops of sugar cane using multidate IRS-1C liss III data: A knowledge based approach. Journal of the Indian Society of Remote Sensing,28, 179–185.
Li, M., Ma, L., Blaschke, T., Cheng, L., & Tiede, D. (2016). A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. International Journal of Applied Earth Observation and Geoinformation,49, 87–98.
Liedtke, C., Bückner, J., Grau, O., Growe, S., & Tönjes, R. (1997). AIDA: A system for the knowledge based interpretation of remote sensing data. Presented at the 3rd international airborne remote sensing conference and exhibition, 7–10 July 1997. Copenhagen: Citeseer.
Liu, W., Gopal, S., & Woodcock, C. E. (2004). Uncertainty and confidence in land cover classification using a hybrid classifier approach. Photogrammetric Engineering and Remote Sensing,70, 963–971.
Liu, X.-H., Skidmore, A., & Van Oosten, H. (2002). Integration of classification methods for improvement of land-cover map accuracy. ISPRS Journal of Photogrammetry and Remote Sensing,56, 257–268.
Liu, Z., Qu, W., Li, H., & Xie, C. (2010). A hybrid collaborative filtering recommendation mechanism for P2P networks. Future Generation Computer Systems,26, 1409–1417.
Lucas, R., Rowlands, A., Brown, A., Keyworth, S., & Bunting, P. (2007). Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping. ISPRS Journal of Photogrammetry and Remote Sensing,62, 165–185.
Ma, L., Li, M., Ma, X., Cheng, L., Du, P., & Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing,130, 277–293.
Markus_Gerke, December 2014. Normalized DSM—Heights encoded in dm—See report for details.
Matsuyama, T. (1987). Knowledge-based aerial image understanding systems and expert systems for image processing. IEEE Transactions on Geoscience and Remote Sensing,25, 305–316.
Maulik, U., & Chakraborty, D. (2011). A self-trained ensemble with semisupervised SVM: An application to pixel classification of remote sensing imagery. Pattern Recognition,44, 615–623.
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing,17, 1425–1432.
McKeown, D. M., Harvey, W. A., & McDermott, J. (1985). Rule-based interpretation of aerial imagery. Pattern Analysis and Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence,12, 570–585.
Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing,66, 247–259.
Mushore, T. D., Mutanga, O., Odindi, J., & Dube, T. (2016). Assessing the potential of integrated Landsat 8 thermal bands, with the traditional reflective bands and derived vegetation indices in classifying urban landscapes. Geocarto International,32, 1–14.
Niemann, H., Sagerer, G. F., Schroder, S., & Kummert, F. (1990). Ernest: A semantic network system for pattern understanding. Pattern Analysis and Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence,12, 883–905.
Rottensteiner, F., Sohn, G., Gerke, M., & Wegner, J. D. (2013). ISPRS test project on urban classification and 3D building reconstruction. In Commission III-photogrammetric computer vision and image analysis, working group III/4-3D scene analysis (pp. 1–17).
Salah, M. (2017). A survey of modern classification techniques in remote sensing for improved image classification. Journal of Geomatics,11, 1–21.
Scikit-Learn. (2018). http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html.
van der Linden, S., Rabe, A., Held, M., Jakimow, B., Leitão, P. J., Okujeni, A., et al. (2015). The EnMAP-Box—A toolbox and application programming interface for EnMAP data processing. Remote Sensing,7, 11249–11266.
Vapnik, V. (2013). The nature of statistical learning theory. Berlin: Springer.
Wan, S., & Lei, T. C. (2009). A knowledge-based decision support system to analyze the debris-flow problems at Chen-Yu-Lan River, Taiwan. Knowledge-Based Systems,22, 580–588.
Waske, B., & van der Linden, S. (2008). Classifying multilevel imagery from SAR and optical sensors by decision fusion. IEEE Transactions on Geoscience and Remote Sensing,46, 1457–1466.
Woźniak, M., Graña, M., & Corchado, E. (2014). A survey of multiple classifier systems as hybrid systems. Information Fusion,16, 3–17.
Zhen, Z., Quackenbush, L. J., Stehman, S. V., & Zhang, L. (2013). Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification. International Journal of Remote Sensing,34, 6914–6930.
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
Kiani, A., Farnood Ahmadi, F. & Ebadi, H. Developing an Interpretation System for High-Resolution Remotely Sensed Images Based on Hybrid Decision-Making Process in a Multi-scale Manner. J Indian Soc Remote Sens 48, 197–214 (2020). https://doi.org/10.1007/s12524-019-01069-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-019-01069-4