VDM-RS: A visual data mining system for exploring and classifying remotely sensed images
Remotely sensed imagery has become increasingly important in several applications domains, such as environmental monitoring, change detection, fire risk mapping and land use, to name only a few. Several advanced image classification techniques have been developed to analyze such imagery and in parti...
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| Published in: | Computers & geosciences Vol. 35; no. 9; pp. 1827 - 1836 |
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| Abstract | Remotely sensed imagery has become increasingly important in several applications domains, such as environmental monitoring, change detection, fire risk mapping and land use, to name only a few. Several advanced image classification techniques have been developed to analyze such imagery and in particular to improve the accuracy of classifying images in the context of such applications. However, most of the proposed classifiers remain a black box to users, leaving them with little to no means to explore and thus further improve the classification process, in particular for misclassified pixel samples. In this paper, we present the concepts, design and implementation of VDM-RS, a visual data mining system for classifying remotely sensed images and exploring image classification processes. The system provides users with two classes of components. First, visual components are offered that are specific to classifying remotely sensed images and provide traditional interfaces, such as a map view and an error matrix view. Second, the decision tree classifier view provides users with the functionality to trace and explore the classification process of individual pixel samples. This feature allows users to inspect how a sample has been correctly classified using the classifier, but more importantly, it also allows for a detailed exploration of the steps in which a sample has been misclassified. The integration of these features into a coherent, user-friendly system not only helps users in getting more insights into the data, but also to better understand and subsequently improve a classifier for remotely sensed images. We demonstrate the functionality of the system's components and their interaction for classifying imagery using a hyperspectral image dataset. |
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| AbstractList | Remotely sensed imagery has become increasingly important in several applications domains, such as environmental monitoring, change detection, fire risk mapping and land use, to name only a few. Several advanced image classification techniques have been developed to analyze such imagery and in particular to improve the accuracy of classifying images in the context of such applications. However, most of the proposed classifiers remain a black box to users, leaving them with little to no means to explore and thus further improve the classification process, in particular for misclassified pixel samples. In this paper, we present the concepts, design and implementation of VDM-RS, a visual data mining system for classifying remotely sensed images and exploring image classification processes. The system provides users with two classes of components. First, visual components are offered that are specific to classifying remotely sensed images and provide traditional interfaces, such as a map view and an error matrix view. Second, the decision tree classifier view provides users with the functionality to trace and explore the classification process of individual pixel samples. This feature allows users to inspect how a sample has been correctly classified using the classifier, but more importantly, it also allows for a detailed exploration of the steps in which a sample has been misclassified. The integration of these features into a coherent, user-friendly system not only helps users in getting more insights into the data, but also to better understand and subsequently improve a classifier for remotely sensed images. We demonstrate the functionality of the system's components and their interaction for classifying imagery using a hyperspectral image dataset. |
| Author | Gruenwald, Le Zhang, Jianting Gertz, Michael |
| Author_xml | – sequence: 1 givenname: Jianting surname: Zhang fullname: Zhang, Jianting email: jzhang@cs.ccny.cuny.edu, zjtnssl@yahoo.com organization: Department of Computer Science, The City College of the City University of New York, New York, NY 10031, USA – sequence: 2 givenname: Le surname: Gruenwald fullname: Gruenwald, Le email: ggruenwald@ou.edu organization: School of Computer Science, The University of Oklahoma, Norman, OK 73019, USA – sequence: 3 givenname: Michael surname: Gertz fullname: Gertz, Michael email: gertz@informatik.uni-heidelberg.de organization: Institute of Computer Science, University of Heidelberg, Heidelberg, Germany |
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| CitedBy_id | crossref_primary_10_3390_rs70709149 crossref_primary_10_1177_14738716241260745 crossref_primary_10_1007_s12650_024_00984_0 crossref_primary_10_1109_TGRS_2012_2199501 crossref_primary_10_1109_TVCG_2018_2864475 crossref_primary_10_1016_j_cageo_2014_09_007 crossref_primary_10_1371_journal_pone_0177438 crossref_primary_10_1080_13658816_2015_1135928 crossref_primary_10_1109_TVCG_2020_3045560 crossref_primary_10_1016_j_cageo_2021_104843 crossref_primary_10_3390_su2051182 |
| Cites_doi | 10.1016/S0198-9715(01)00046-1 10.1109/TVCG.2003.1207445 10.1057/palgrave.ivs.9500057 10.1109/2945.841119 10.1016/j.cageo.2004.11.009 10.1016/j.ijhcs.2006.07.005 10.1016/S0167-9473(02)00295-5 10.1109/TGRS.2004.837325 10.1109/LGRS.2006.887142 10.1016/S0098-3004(02)00031-6 10.1016/S0034-4257(00)00142-5 10.1080/13658810600607550 10.1016/S0034-4257(97)00049-7 10.1109/ICDM.2006.121 10.1016/j.rse.2005.11.003 10.1109/TGRS.2007.892008 10.1145/253262.253335 10.1109/TVCG.2006.84 10.1109/TGRS.2004.831865 10.1109/TGRS.2005.847908 10.1080/01431160600746456 10.1016/S0034-4257(02)00078-0 10.1109/2945.981847 10.1057/palgrave.ivs.9500065 10.1109/LGRS.2005.844658 10.1109/36.803413 10.1145/956804.956837 |
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| Keywords | Visualization Data mining Decision tree Remote sensing Image classification detection interfaces exploration maps Space remote sensing data processing accuracy remote sensing classification North America image analysis spatial distribution decision support systems fires land use imagery decision making errors computers Pixel |
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| Title | VDM-RS: A visual data mining system for exploring and classifying remotely sensed images |
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