Automatic Image Classification for Planetary Exploration

Autonomous techniques in the context of planetary exploration can maximize scientific return and reduce the need for human involvement. This thesis work studies two main problems in planetary exploration: rock image classification and hyperspectral image classification. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. The proposed feature method is flexible and can outperform manually selected features. In order to take advantage of the unlabelled rock images, we also propose self-taught learning technique to learn the feature representation from unlabelled rock images and then apply the features for the classification of the subclass of rock images. Since combining spatial information with spectral information for classifying hyperspectral images (HSI) can dramatically improve the performance, we first propose an innovative framework to automatically generate spatial-spectral features for HSI. Two unsupervised learning methods, K-means and PCA, are utilized to learn the spatial feature bases in each decorrelated spectral band. Then spatial-spectral features are generated by concatenating the spatial feature representations in all/principal spectral bands. In the second work for HSI classification, we propose to stack the spectral patches to reduce the spectral dimensionality and generate 2-D spectral quilts. Such quilts retain all the spectral information and can result in less convolutional parameters in neural networks. Two light convolutional neural networks are then designed to classify the spectral quilts. As the third work for HSI classification, we propose a combinational fully convolutional network. The network can not only take advantage of the inherent computational efficiency of convolution at prediction time, but also perform as a collection of many paths and has an ensemble-like behavior which guarantees the robust performance

Soil Characterization and Classification: A Hybrid Approach of Computer Vision and Sensor Network

This paper presents soil characterization and classification using computer vision & sensor network approach. Gravity Analog Soil Moisture Sensor with arduino-uno and image processing is considered for classification and characterization of soils. For the data sets, Amhara regions and Addis Ababa city of Ethiopia are considered for this study. In this research paper the total of 6 group of soil and each having 90 images are used. That is, form these 540 images were captured. Once the dataset is collected, pre-processing and noise filtering steps are performed to achieve the goal of the study through MATLAB, 2013. Classification and characterization is performed through BPNN (Back-propagation neural network), the neural network consists of 7 inputs feature vectors and 6 neurons in its output layer to classify soils. 89.7% accuracy is achieved when back-propagation neural network (BPNN) is used

Autonomous and Real Time Rock Image Classification using Convolutional Neural Networks

Autonomous image recognition has numerous potential applications in the field of planetary science and geology. For instance, having the ability to classify images of rocks would allow geologists to have immediate feedback without having to bring back samples to the laboratory. Also, planetary rovers could classify rocks in remote places and even in other planets without needing human intervention. In 2017, Shu et. al. used a Support Vector Machine (SVM) classification algorithm to classify 9 different types of rock images using a with the image features extracted autonomously. Through this method, they achieved a test accuracy of 96.71%. Within the last few years, Convolutional Neural Networks (CNNs) have been shown to be perform better than other algorithms in classifying images of everyday objects. In light of this development, this thesis demonstrates the use of CNNs to classify the same set of rock images. With the addition of dataset augmentation, a 3-layer CNN is shown to have a significant improvement over Shu et. al.\u27s results, achieving an average accuracy of 99.60% across 10 trials on the test set. Multiple CNN operations with similar output shapes have been designed and appended to an existing architecture to expand hyperparameter considerations. These Combinational Fully Connected Neural Networks achieves an accuracy of 99.36% on the test set. The resulting models are also shown to be lightweight enough that they can be deployed on a mobile device. To tackle a more interesting and practical problem, CNNs have also been designed to classify natural scene images of rocks, an inherently more complex dataset. The task has been simplified into a binary classification problem where the images are classified into breccia and non-breccia. This thesis shows that a Combinational Fully Connected Neural Network achieves an accuracy of 93.50%, better than a 5-layer CNN, which achieves 89.43%

The effects of multiple layers feed-forward neural network transfer function in digital based Ethiopian soil classification and moisture prediction

In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh

Global and local characterization of rock classification by Gabor and DCT filters with a color texture descriptor

In the automatic classification of colored natural textures, the idea of proposing methods that reflect human perception arouses the enthusiasm of researchers in the field of image processing and computer vision. Therefore, the color space and the methods of analysis of color and texture, must be discriminating to correspond to the human vision. Rock images are a typical example of natural images and their analysis is of major importance in the rock industry. In this paper, we combine the statistical (Local Binary Pattern (LBP) with Hue Saturation Value (HSV) and Red Green Blue (RGB) color spaces fusion) and frequency (Gabor filter and Discrete Cosine Transform (DCT)) descriptors named respectively Gabor Adjacent Local Binary Pattern Color Space Fusion (G-ALBPCSF) and DCT Adjacent Local Binary Pattern Color Space Fusion (D-ALBPCSF) for the extraction of visual textural and colorimetric features from direct view images of rocks. The textural images from the two G-ALBPCSF and D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2 and the intersection of histograms that we have adapted to color histograms. The results obtained allowed us to highlight the discrimination of the rock classes. The proposed extraction method provides better classification results for various direct view rock texture images. Then it is validated by a confusion matrix giving a low error rate of 0.8% of classification

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

Developing A Machine Learning Based Approach For Fractured Zone Detection By Using Petrophysical Logs

Oil reservoirs are divided into three categories: carbonate (fractured), sandstone and unconventional reservoirs. Identification and modeling of fractures in fractured reservoirs are so important due to geomechanical issues, fluid flood simulation and enhanced oil recovery.Image and petrophysical logs are individual tools, run inside oil wells, to achieve physical characteristics of reservoirs, e.g. geological rock types, porosity, and permeability. Fractures could be distinguished using image logs because of their higher resolution. Image logs are an expensive and newly developed tool, so they have run in limited wells, whereas petrophysical logs are usually run inside the wells. Lack of image logs makes huge difficulties in fracture detection, as well as fracture studies. In the last decade, a few studies were done to distinguish fractured zones in oil wells, by applying data mining methods over petrophysical logs. The goal of this study was also discrimination of fractured/non-fractured zones by using machine learning techniques and petrophysical logs. To do that, interpretation of image logs was utilized to label reservoir depth of studied wells as 0 (non-fractured zone) and 1 (fractured zone). We developed four classifiers (Deep Learning, Support Vector Machine, Decision Tree, and Random Forest) and applied them to petrophysics logs to discriminate fractured/non-fractured zones. Ordered Weighted Averaging was the data fusion method that we utilized to integrate outputs of classifiers in order to achieve unique and more reliable results. Overall, the frequency of non-fractured zones is about two times of fractured zones. This leads to an imbalanced condition between two classes. Therefore, the aforementioned procedure relied on the balance/imbalance data to investigate the influence of creating a balanced situation between classes. Results showed that Random Forest and Support Vector Machines are better classifiers with above 95 percent accuracy in discrimination of fractured/non-fractured zones. Meanwhile, making a balanced situation in the wells by a higher imbalance index helps to distinguish either non-fractured or fractured zones. Through imbalance data, non-fractured zones (dominant class) could be perfectly distinguished, while a significant percentage of fractured zones were also labeled as non-fractured ones