Multiple Classifier System for Remote Sensing Image Classification: A Review

Du, Peijun; Xia, Junshi; Zhang, Wei; Tan, Kun; Liu, Yi; Liu, Sicong

doi:10.3390/s120404764

Cited by 266 publications

(155 citation statements)

References 63 publications

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“…Modifications of the confusion matrix for compatibility with fuzzy classification have been proposed (Binaghi et al, 1999) but studies of fuzzy classification still continue to be published with non-fuzzy evalution (Du et al, 2012). Various indices for representing classification certainty from the class membership vector in each pixel have been proposed (Maselli et al, 1994;Prasad and Arora, 2014), which now allow spatially explicit representation of certainty or graphic visualization independent from position in space.…”

Section: State Of the Artmentioning

confidence: 99%

Will It Blend? Visualization and Accuracy Evaluation of High-Resolution Fuzzy Vegetation Maps

Zlinszky¹,

Kania²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Instead of assigning every map pixel to a single class, fuzzy classification includes information on the class assigned to each pixel but also the certainty of this class and the alternative possible classes based on fuzzy set theory. The advantages of fuzzy classification for vegetation mapping are well recognized, but the accuracy and uncertainty of fuzzy maps cannot be directly quantified with indices developed for hard-boundary categorizations. The rich information in such a map is impossible to convey with a single map product or accuracy figure. Here we introduce a suite of evaluation indices and visualization products for fuzzy maps generated with ensemble classifiers. We also propose a way of evaluating classwise prediction certainty with "dominance profiles" visualizing the number of pixels in bins according to the probability of the dominant class, also showing the probability of all the other classes. Together, these data products allow a quantitative understanding of the rich information in a fuzzy raster map both for individual classes and in terms of variability in space, and also establish the connection between spatially explicit class certainty and traditional accuracy metrics. These map products are directly comparable to widely used hard boundary evaluation procedures, support active learning-based iterative classification and can be applied for operational use.

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Section: State Of the Artmentioning

confidence: 99%

Will It Blend? Visualization and Accuracy Evaluation of High-Resolution Fuzzy Vegetation Maps

Zlinszky¹,

Kania²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Traditional methods like k-nearest neighbor (k-NN) or maximum likelihood (ML) have been used frequently in the past, but are nowadays increasingly replaced by modern and robust supervised machine learning algorithms including tree-based methods, artificial neural networks or support vector machines. Several studies compared the performance of machine learning algorithms with OBIA or pixel-based classification [31,34,35]. However, it is yet unclear which of the classifiers performs the best with OBIA and pixel-based approaches, especially when used with VHR imagery for detail separation of vegetation components in African savanna.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms

et al. 2016

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Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify land cover components of African savanna in wet and dry season. We compared the performance of Object-Based Image Analysis (OBIA) and pixel-based approach with several algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification and regression trees (CART) and support vector machines (SVM). Results showed that classifications of WV-2 imagery produce high accuracy results (>77%) regardless of the applied classification approach. However, OBIA had a significantly higher accuracy for almost every classifier with the highest overall accuracy score of 93%. Amongst tested classifiers, SVM and RF provided highest accuracies. Overall classifications of the wet season image provided better results with 93% for RF. However, considering woody leaf-off conditions, the dry season classification also performed well with overall accuracy of 83% (SVM) and high producer accuracy for the tree cover (91%). Our findings demonstrate the potential of imagery like WorldView-2 with OBIA and advanced supervised machine-learning algorithms in seasonal fine-scale land cover classification of African savanna.

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“…In the case of majority voting, the output of the ensemble is the most assigned class by classifiers, whereas in the weighted majority voting rule, a weight is assigned to each classifier to favor those classifiers with better performance in the voting decision. Both rules are easily implemented and produce results comparable to more complicated combination schemes [30,36,70]. Moreover, the se rules do not require additional training data because they are not trainable [40] which means that the required parameters for the ensemble are available as the classifiers are generated and their accuracy assessed.…”

Section: Ensemble Classifiersmentioning

confidence: 99%

“…Given the increasing availability of computing resources, various studies have shown that ensemble classifiers outperform individual classifiers [30][31][32]. Yet, the use of ensemble classifiers remains scarce in the context of remote sensing [33] and is limited to image subsets, mono-temporal studies, or to the combination of only a few classifiers [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

et al. 2018

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Smallholder farmers cultivate more than 80% of the cropland area available in Africa. The intrinsic characteristics of such farms include complex crop-planting patterns, and small fields that are vaguely delineated. These characteristics pose challenges to mapping crops and fields from space. In this study, we evaluate the use of a cloud-based multi-temporal ensemble classifier to map smallholder farming systems in a case study for southern Mali. The ensemble combines a selection of spatial and spectral features derived from multi-spectral Worldview-2 images, field data, and five machine learning classifiers to produce a map of the most prevalent crops in our study area. Different ensemble sizes were evaluated using two combination rules, namely majority voting and weighted majority voting. Both strategies outperform any of the tested single classifiers. The ensemble based on the weighted majority voting strategy obtained the higher overall accuracy (75.9%). This means an accuracy improvement of 4.65% in comparison with the average overall accuracy of the best individual classifier tested in this study. The maximum ensemble accuracy is reached with 75 classifiers in the ensemble. This indicates that the addition of more classifiers does not help to continuously improve classification results. Our results demonstrate the potential of ensemble classifiers to map crops grown by West African smallholders. The use of ensembles demands high computational capability, but the increasing availability of cloud computing solutions allows their efficient implementation and even opens the door to the data processing needs of local organizations.

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Multiple Classifier System for Remote Sensing Image Classification: A Review

Cited by 266 publications

References 63 publications

Will It Blend? Visualization and Accuracy Evaluation of High-Resolution Fuzzy Vegetation Maps

Will It Blend? Visualization and Accuracy Evaluation of High-Resolution Fuzzy Vegetation Maps

Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

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