Robust Features for Snapshot Hyperspectral Terrain-Classification

Winkens, Christian; Kobelt, Volkmar; Paulus, Dietrich

doi:10.1007/978-3-319-64689-3_2

Cited by 8 publications

(9 citation statements)

References 22 publications

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“…The problem they solve is the difficulty of labeling training data for AI algorithms. Similarly, references [ 31 , 32 , 33 , 34 , 35 ] are about environmental classification, not navigation.…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral Imaging for Mobile Robot Navigation

Jakubczyk

Siemiątkowska

Więckowski

et al. 2022

Sensors

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The article presents the application of a hyperspectral camera in mobile robot navigation. Hyperspectral cameras are imaging systems that can capture a wide range of electromagnetic spectra. This feature allows them to detect a broader range of colors and features than traditional cameras and to perceive the environment more accurately. Several surface types, such as mud, can be challenging to detect using an RGB camera. In our system, the hyperspectral camera is used for ground recognition (e.g., grass, bumpy road, asphalt). Traditional global path planning methods take the shortest path length as the optimization objective. We propose an improved A* algorithm to generate the collision-free path. Semantic information makes it possible to plan a feasible and safe path in a complex off-road environment, taking traveling time as the optimization objective. We presented the results of the experiments for data collected in a natural environment. An important novelty of this paper is using a modified nearest neighbor method for hyperspectral data analysis and then using the data for path planning tasks in the same work. Using the nearest neighbor method allows us to adjust the robotic system much faster than using neural networks. As our system is continuously evolving, we intend to examine the performance of the vehicle on various road surfaces, which is why we sought to create a classification system that does not require a prolonged learning process. In our paper, we aimed to demonstrate that the incorporation of a hyperspectral camera can not only enhance route planning but also aid in the determination of parameters such as speed and acceleration.

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Section: Related Workmentioning

confidence: 99%

Hyperspectral Imaging for Mobile Robot Navigation

Jakubczyk

Siemiątkowska

Więckowski

et al. 2022

Sensors

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show abstract

“…1c and 1d are manufactured by IMEC [7]. They are embedded in a few off-the-shelf MSFA-based devices available on the market, like XIMEA xiSpec and IMEC "snapshot mosaic" multispectral cameras, with applications in medical imaging [8] or terrain classification [9]. The 4 × 4 basic pattern samples 16 bands centered at wavelengths λ 1 = 469 nm, .…”

Section: A Multispectral Filter Arraysmentioning

confidence: 99%

Spatio-spectral binary patterns based on multispectral filter arrays for texture classification

et al. 2018

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To discriminate gray-level texture images, spatial texture descriptors can be extracted using the local binary pattern (LBP) operator. This operator has been extended to color images at the expense of increased memory and computation requirements. Some authors propose to compute texture descriptors directly from raw images provided through a Bayer color filter array, which both avoids the demosaicking step and reduces the descriptor size. Recently, multispectral snapshot cameras have emerged to sample more than three wavelength bands using a multispectral filter array. Such cameras provide a raw image in which a single spectral channel value is available at each pixel. In this paper we design a local binary pattern operator that jointly extracts the spatial and spectral texture information directly from a raw image. Extensive experiments on a large dataset show that the proposed descriptor has both reduced computation cost and high discriminative power with regard to classical LBP descriptors applied to demosaicked images.

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“…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 ,…”

Section: Introductionmentioning

confidence: 99%

“…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 ].…”

Section: Introductionmentioning

confidence: 99%

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

Gao

et al. 2022

Entropy

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It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%.

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Robust Features for Snapshot Hyperspectral Terrain-Classification

Cited by 8 publications

References 22 publications

Hyperspectral Imaging for Mobile Robot Navigation

Hyperspectral Imaging for Mobile Robot Navigation

Spatio-spectral binary patterns based on multispectral filter arrays for texture classification

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

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