Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUTN₅₀) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven- and eight-band combinations, the LUTN₅₀ approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUTN50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications

2D-SSA based multiscale feature fusion for feature extraction and data classification in hyperspectral imagery

Singular spectrum analysis (SSA) and its 2-D variation (2D-SSA) have been successfully applied for effective feature extraction in hyperspectral imaging (HSI). However, they both cannot effectively use the spectral-spatial information, leading to a limited accuracy in classification. To tackle this problem, a novel 2D-SSA based multiscale feature fusion method, combining with segmented principal component analysis (SPCA), is proposed in this paper. The SPCA method is used for dimension reduction and spectral feature extraction, while multiscale 2D-SSA can extract abundant spatial features at different scales. In addition, a postprocessing via SPCA is applied on fused features to enhance the spectral discriminability. Experiments on two widely used datasets show that the proposed method outperforms two conventional SSA methods and other spectral-spatial classification methods in terms of the classification accuracy and computational cost

Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Although singular spectrum analysis (SSA) has been successfully applied for data classification in hyperspectral remote sensing, it suffers from extremely high computational cost, especially for 2D-SSA. As a result, a fast implementation of 2D-SSA namely F-2D-SSA is presented in this paper, where the computational complexity has been significantly reduced with a rate up to 60%. From comprehensive experiments undertaken, the effectiveness of F-2D-SSA is validated producing a similar high-level of accuracy in pixel classification using support vector machine (SVM) classifier, yet with a much reduced complexity in comparison to conventional 2D-SSA. Therefore, the introduction and evaluation of F-2D-SSA completes a series of studies focused on SSA, where in this particular research, the reduction in computational complexity leads to potential applications in mobile and embedded devices such as airborne or satellite platforms

Extensive Huffman-tree-based neural network for the imbalanced dataset and its application in accent recognition

To classify the data-set featured with a large number of heavily imbalanced classes, this thesis proposed an Extensive Huffman-Tree Neural Network (EHTNN), which fabricates multiple component neural network-enabled classifiers (e.g., CNN or SVM) using an extensive Huffman tree. Any given node in EHTNN can have arbitrary number of children. Compared with the Binary Huffman-Tree Neural Network (BHTNN), EHTNN may have smaller tree height, involve fewer component neural networks, and demonstrate more flexibility on handling data imbalance. Using a 16-class exponentially imbalanced audio data-set as the benchmark, the proposed EHTNN was strictly assessed based on the comparisons with alternative methods such as BHTNN and single-layer CNN. The experimental results demonstrated promising results about EHTNN in terms of Gini index, Entropy value, and the accuracy derived from hierarchical multiclass confusion matrix

Feature extraction of hyperspectral images using boundary semi-labeled samples and hybrid criterion

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new feature extraction method in this paper, which uses the boundary semi-labeled samples for solving small sample size problem. The proposed method, which called hybrid feature extraction based on boundary semi-labeled samples (HFE-BSL), uses a hybrid criterion that integrates both the local and global criteria for feature extraction. Thus, it is robust and flexible. The experimental results with three real hyperspectral images show the good efficiency of HFE-BSL compared to some popular and state-of-the-art feature extraction methods

Smart information retrieval: domain knowledge centric optimization approach

In the age of Internet of Things (IoT), online data has witnessed significant growth in terms of volume and diversity, and research into information retrieval has become one of the important research themes in the Internet oriented data science research. In information retrieval, machine-learning techniques have been widely adopted to automate the challenging process of relation extraction from text data, which is critical to the accuracy and efficiency of information retrieval-based applications including recommender systems and sentiment analysis. In this context, this paper introduces a novel, domain knowledge centric methodology aimed at improving the accuracy of using machine-learning methods for relation classification, and then utilise Genetic Algorithms (GAs) to optimise the feature selection for the learning algorithms. The proposed methodology makes significant contribution to the processes of domain knowledge-based relation extraction including interrogating Linked Open Datasets to generate the relation classification training-data, addressing the imbalanced classification in the training datasets, determining the probability threshold of the best learning algorithm, and establishing the optimum parameters for the genetic algorithm utilised in feature selection. The experimental evaluation of the proposed methodology reveals that the adopted machine-learning algorithms exhibit higher precision and recall in relation extraction in the reduced feature space optimised by the implementation. The considered machine learning includes Support Vector Machine, Perceptron Algorithm Uneven Margin and K-Nearest Neighbours. The outcome is verified by comparing against the Random Mutation Hill-Climbing optimisation algorithm using Wilcoxon signed-rank statistical analysis

Detection and mapping of small-scale and slow-moving landslides from very high resolution optical satellite data

Small slope failures are often ignored because of their perceived less severe impact. Although they may have small velocity, small slope failures can cause damages to facilities such roads and pipelines. The main objective of this research is to utilise very high resolution Pleiades-1 data to extract surface features and identify surface deformations susceptible to small slope failures. An algorithm was developed using object-based image analysis (OBIA), Pleiades-1 imagery, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) and Real Time Kinematic-Global Positioning System (RTK-GPS) data. Using the OBIA algorithm four different object attribute parameters namely spectral, textural, spatial and topographic characteristics were applied in a rule-based classification, for semi-automated detection of small translational landslides. The developed OBIA algorithm was further modified by using Pleiades-1 imagery, Nearest Neighbors (k-NN) and Support Vector Machine (SVM) techniques in example-based classification for the detection of small landslides, with focus on the effects of the training samples size and type on the results of the classification. The horizontal displacement of the landslides was investigated based on sub-pixel image correlation method using Pleiades-1 images and Shuttle Radar Topographic Mission (SRTM). Kalman filtering method and RTK-GPS observations from TUSAGA-Aktif Global Navigation Satellite System (GNSS) Network in Turkey were utilised to formulate kinematic analysis model for the landslides. The developed algorithms were validated in Kutlugün test site in Northeastern Turkey. In the rule-based classification results, a total of 123 small landslides covering a total area of approximately 413.332 m2 were detected. The size of landslides detected varied between 0.747 and 7.469 m2. The detected landslides yielded user’s accuracy of 81.8%, producer’s accuracy of 80.6%, quality percentage of 82% and computed kappa index of 0.87. In the small landslides detection using the example-based classification, the SVM method had higher producer accuracy (85.9%), user accuracy (89.4%) and kappa index (0.82) compared to the k-NN algorithm that had producer accuracy (83.1%), user accuracy (86.0%) and kappa index (0.80). A total of 128 small landslides were detected using k-NN algorithm, while a total of 134 landslides were detected using SVM algorithm. The displacement results from RTK-GPS measurements varied from 2.77 mm to 24.87 mm in 6 months, while the velocities varied from 0.80 mm to 8.28 mm/6 month. The displacements from optical image correlation agreed well with RTK-GPS results and provided a more uniform movement pattern than could be derived solely using the RTK-GPS measurements. The landslide movements are dominantly toward the north direction. These trends agree with the results of previous study in the area. The main contributions of this research include – development of a comprehensive metrics to quantify the attribute parameters of small landslides, derivation of susceptibility and inventory maps for small landslides, and the design of an early warning system for small slope failures on highway infrastructures. The results of this research will add to the increasing applications of Pleiades-1 image in landslide investigations

Using Penalized Linear Discriminant Analysis and Normalized Difference Indices Derived from Landsat 8 Images to Classify Fruit-tree Crops in the Aconcagua Valley, Chile

Accurate crop type maps are critical for yield estimation and agricultural practices in modern agriculture. A new approach is proposed in this thesis to improve the crop type classification accuracy, by creating a new feature set containing new spectral indices in addition to basic bands. Two types of penalized linear discriminant analysis classifiers are adopted to do the classification, and the cross-validated classification accuracies on the two different feature sets are compared to see whether the new feature set can improve the crop identification. The result shows with new indices in the feature set the classification mean error rates were decreased substantially for both classifiers (21.6% and 25.2%). Through analyzing the coefficients retrieved from the best model, the variable importance was assessed. The coefficients are summarized by different bands and images, and the result suggest that red and shortwave infrared are the two bands highly related to the fruit-trees type identification in the study area in Aconcagua valley, Chile. Also late winter to early spring may be the best time to do crop type mapping for these crop types