Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection

Shang, Changjing; Barnes, Dave; Shen, Qiang

doi:10.3233/his-2011-0126

Cited by 17 publications

(16 citation statements)

References 31 publications

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“…Many studies have focused explicitly on the relationship between these two bands (e.g., [67][68][69][70]), and developing indices for interpreting spectral and land information space. A few studies have also applied algorithmic classification techniques (both supervised and unsupervised) to Martian datasets, including: hyperspectral imagery and mineralogy data from orbital measurements [71][72][73][74][75][76][77] and ground-measurements by the rovers [78]; terrain mapping and feature classification from elevation and surface roughness data [79][80][81][82] and visual imagery [83]; and automated detection of impact craters [84,85]. The use of algorithmic classification in studies of Mars is increasing over time, however no previous study has applied algorithmic classification to mapping surface grain size and thermal behaviour in Martian thermal inertia and albedo data.…”

Section: -22]mentioning

confidence: 99%

An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques

et al. 2014

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Thermal inertia and albedo provide information on the distribution of surface materials on Mars. These parameters have been mapped globally on Mars by the Thermal Emission Spectrometer (TES) onboard the Mars Global Surveyor. Two-dimensional clusters of thermal inertia and albedo reflect the thermophysical attributes of the dominant materials on the surface. In this paper three automated, non-deterministic, algorithmic classification methods are employed for defining thermophysical units: Expectation Maximisation of a Gaussian Mixture Model; Iterative Self-Organizing Data Analysis Technique (ISODATA); and Maximum Likelihood. We analyse the behaviour of the OPEN ACCESS Remote Sens. 2014, 6 5185 thermophysical classes resulting from the three classifiers, operating on the 2007 TES thermal inertia and albedo datasets. Producing a rigorous mapping of thermophysical classes at ~3 km/pixel resolution remains important for constraining the geologic processes that have shaped the Martian surface on a regional scale, and for choosing appropriate landing sites. The results from applying these algorithms are compared to geologic maps, surface data from lander missions, features derived from imaging, and previous classifications of thermophysical units which utilized manual (and potentially more time consuming) classification methods. These comparisons comprise data suitable for validation of our classifications. Our work shows that a combination of the algorithms-ISODATA and Maximum Likelihood-optimises the sensitivity to the underlying dataspace, and that new information on Martian surface materials can be obtained by using these methods. We demonstrate that the algorithms used here can be applied to define a finer partitioning of albedo and thermal inertia for a more detailed mapping of surface materials, grain sizes and thermal behaviour of the Martian surface and shallow subsurface, at the ~3 km scale.

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Section: -22]mentioning

confidence: 99%

An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques

et al. 2014

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“…This is very important to ensure that the classification results are understandable by the user. It is of particular significance for classification and analysis of real-world images in the areas of medical analysis and space engineering [27]. Indeed, such applications remain as active research.…”

Section: Discussionmentioning

confidence: 99%

“…However, generating more features increases the computational complexity as well as the measurement noise. This may cause problems in many application domains such as on-board processing of Mars images where demand for computational memory and processing time must be minimised [27] (despite the nowadays generally available and relatively cheap computer power). In addition, not all such features may be useful to perform classification [12], [15], [23], [25].…”

Section: Introductionmentioning

confidence: 99%

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Support vector machine-based classification of rock texture images aided by efficient feature selection

Shang¹,

Barnes²

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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Abstract-This paper presents a study on rock texture image classification using support vector machines (and also K-nearest neighbours and decision trees) with the aid of feature selection techniques. It offers both unsupervised and supervised methods for feature selection, based on data reliability and information gain ranking respectively. Following this approach, the conventional classifiers which are sensitive to the dimensionality of feature patterns, become effective on classification of images whose pattern representation may otherwise involve a large number of features. The work is successfully applied to complex images. Classifiers built using features selected by either of these methods generally outperform their counterparts that employ the full set of original features which has a dimensionality several folds higher than that of the selected feature subset. This is confirmed by systematic experimental investigations. This study therefore, helps to accomplish challenging image classification tasks effectively and efficiently. In particular, the approach retains the underlying semantics of a selected feature subset. This is very important to ensure that the classification results are understandable by the user.

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“…Human evaluation and subsequent pattern identification is also limited when considering such datasets [58]. Techniques to perform tasks such as text processing, data classification and systems control [32,38,46,47] can benefit greatly from FS, once the noisy, irrelevant, redundant or misleading features are removed [22].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Ensemble

Shen¹,

Diao²,

Su³

EPiC Series in Computing

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Many strategies have been exploited for the task of feature selection, in an effort to identify more compact and better quality feature subsets. Such techniques typically involve the use of an individual feature significance evaluation, or a measurement of feature subset consistency, that work together with a search algorithm in order to determine a quality subset. Feature selection ensemble aims to combine the outputs of multiple feature selectors, thereby producing a more robust result for the subsequent classifier learning tasks. In this paper, three novel implementations of the feature selection ensemble concept are introduced, generalising the ensemble approach so that it can be used in conjunction with many subset evaluation techniques, and search algorithms. A recently developed heuristic algorithm: harmony search is employed to demonstrate the approaches. Results of experimental comparative studies are reported in order to highlight the benefits of the present work. The paper ends with a proposal to extend the application of feature selection ensemble to aiding the development of biped robots (inspired by the authors' involvement in the joint celebration of Olympic and the centenary of the birth of Alan Turing).

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Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection

Cited by 17 publications

References 31 publications

An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques

An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques

Support vector machine-based classification of rock texture images aided by efficient feature selection

Feature Selection Ensemble

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