Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images.

To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel level. First, the HSI is divided into different spatial regions, each region is shape- and size-adapted and considered as a superpixel. For each superpixel, it contains a number of pixels with similar spectral characteristic. Since the utilization of multiple features in HSI classification has been proved to be an effective strategy, we have generated both spatial and spectral features for each superpixel. By assuming that all the pixels in a superpixel belongs to one certain class, a kernel SRC is introduced to the classification of HSI. In the SRC framework, we have employed a metric learning strategy to exploit the commonalities of different features. Experimental results on two popular HSI datasets have demonstrated the efficacy of our proposed methodology

Feature Extraction using Histogram of Oriented Gradients for Image Classification in Maize Leaf Diseases

The paper presents feature extraction methods and classification algorithms used to classify maize leaf disease images. From maize disease images, features are extracted and passed to the machine learning classification algorithm to identify the possible disease based on the features detected using the feature extraction method. The maize disease images used include images of common rust, leaf spot, and northern leaf blight and healthy images. An evaluation was done for the feature extraction method to see which feature extraction method performs best with image classification algorithms. Based on the evaluation, the outcomes revealed Histogram of Oriented Gradients performed best with classifiers compared to KAZE and Oriented FAST and rotated BRIEF. The random forest classifier emerged the best in terms of image classification, based on four performance metrics which are accuracy, precision, recall, and F1-score. The experimental outcome indicated that the random forest had 0.74 accuracy, 0.77 precision, 0.77 recall, and 0.75 F1-score

Feature Extraction using Histogram of Oriented Gradients for Image Classification in Maize Leaf Diseases

The paper presents feature extraction methods and classification algorithms used to classify maize leaf disease images. From maize disease images, features are extracted and passed to the machine learning classification algorithm to identify the possible disease based on the features detected using the feature extraction method. The maize disease images used include images of common rust, leaf spot, and northern leaf blight and healthy images. An evaluation was done for the feature extraction method to see which feature extraction method performs best with image classification algorithms. Based on the evaluation, the outcomes revealed Histogram of Oriented Gradients performed best with classifiers compared to KAZE and Oriented FAST and rotated BRIEF. The random forest classifier emerged the best in terms of image classification, based on four performance metrics which are accuracy, precision, recall, and F1-score. The experimental outcome indicated that the random forest had 0.74 accuracy, 0.77 precision, 0.77 recall, and 0.75 F1-score

Comparison of Supervised Image Classification Algorithms: Classifying Diverse Land Cover in California

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe research field of machine learning and supervised image classification is quickly developing. There are many studies regarding the different use cases of image classification. However, a comprehensive study on the primary algorithms in ArcGIS Pro has not been assessed for numerous classes. This study attempts to bridge that gap by evaluating the effectiveness of the three primary classification algorithms available in ArcGIS Pro, and to determine an optimal algorithm for the given study area. This scope covers 12 classes of land cover in San Joaquin County, California. Maximum Likelihood, Random Forest, and Support Vector Machine were tested based on their general usability in image classification as well as their proven characteristics through research. The training and ground truth validation data were provided by USGS, in the form of a Landsat 8 image, and crop planning map. The accuracy assessment was performed with a stratified random sampling strategy. Based on the Kappa statistic, this study determines Random Forest (Kappa = 0.68, Accuracy = 0.76) to be the most suitable algorithm for detecting a series of crop types, bodies of water, and urban spaces apart from the rest of the land cover in San Joaquin County, California, USA. In addition to determining a preferred algorithm, it is also apparent that certain parameters when tweaked, produce the optimal classifier for this dataset. In this case, this means most parameters set to default, with an increased spectral detail and a decreased spatial detail. What this indicates for crop planning is that the current algorithms used in California are already quite effective at accurately identifying unique types of land cover. This builds confidence in the field, however parameters could be similarly tweaked to produce an even better classification. This study can be useful for improving crop and water planning

Machine Learning Methods for Septic Shock Prediction

Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify high-risk patients. A robust prediction model is built by obtaining a risk score from fitting the Cox Hazard model on multiple input features. The score is added to the list of features and the Random Forest ensemble classifier is trained to produce the model. The Cox Enhanced Random Forest (CERF) proposed method is evaluated by comparing its predictive accuracy to those of extant methods