Effective Feature Selection for Mars McMurdo Terrain Image Classification

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

“…This paper presents an integrated approach for performing large-scale Mars terrain image classification, by exploiting the recent advances in fuzzy-rough feature selection techniques [15]. It is based on the initial work presented in [28,29], where fuzzy-rough methods were, for the first time, applied to tasks relevant to space engineering. In this work, only those informative features are required to be generated in order to perform classification.…”

Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features

“…For the difficulty of data acquisition for Mars terrain images, many studies tested terrain classification methods with roves’ fully operational duplicates in Earth conditions and then applied those methods to the actual rover. The image features that are often used for terrain classification in those studies include color features based on the RGB space [ 8 , 9 , 10 , 11 , 12 ], HSV [ 6 , 7 , 13 , 14 , 15 ], and Lab [ 16 ] spaces; Gabor features [ 12 , 17 , 18 ]; the contrast [ 10 , 11 , 12 ], correlation [ 12 ], energy [ 11 , 12 , 13 ], and consistency [ 12 ] of gray-level co-occurrence matrix (GLCM); SURF features [ 19 , 20 ]; Daisy features [ 19 , 20 ]; local binary patterns (LBP) [ 19 , 20 , 21 ]; local ternary patterns (LTP) [ 19 , 20 , 21 ]; local adaptive ternary patterns (LATP) [ 19 , 20 ]; contrast context histogram (CCH) [ 20 ]; and the mean [ 2 , 9 , 13 , 14 , 15 , 22 ], entropy [ 8 , 9 , 22 ], contrast [ 8 , 23 ], correlation [ 23 ], energy [ 8 ,…”

“…It will reduce the classification accuracy for terrain classification. In these studies, the classifiers used include random forests (RFs) [ 2 , 12 , 18 , 19 , 20 , 21 ], SVMs [ 6 , 7 , 8 , 9 , 16 , 17 , 19 , 20 ], multilayer perceptron [ 13 , 14 , 15 , 19 , 20 ], LIBLINEAR [ 19 , 20 ], decision tree [ 19 , 20 ], naïve Bayes classifier [ 19 , 20 ], K-nearest neighbor (KNN) [ 13 , 14 , 15 , 17 , 19 , 20 ], extreme learning machine [ 17 , 24 ], batch-incremental regression tree model [ 22 ], probabilistic neural network [ 23 ], and multilayer feed forward neural network learning algorithm [ 10 ].…”

“…Several published studies have focused on Mars terrain classification [ 2 , 13 , 14 , 15 ]. Ono [ 2 ] used a set of NAVCAM images from MSL to study Mars terrain classification and extracted the following image features in gray, gradients, and range channels: the channel intensity and the intensity at an x and y offset from the pixel and the averages of the rectangles at random positions in the local context of the point of interest.…”

“…RF is used to classify each pixel in the terrain image with an accuracy of the derivable terrain of 76.2%. Shang [ 13 , 14 , 15 ] investigated Mars terrain classification using a Plate South panorama image obtained from the panoramic camera on the Spirit rover. A fuzzy rough feature selection was applied to the color and gray statistics features to select essential informative features in representing Mars terrain images.…”