Majid Mahrooghy

Decision Fusion of Textural Features Derived From Polarimetric Data for Levee Assessment

Texture features derived from Synthetic Aperture Radar (SAR) imagery using grey level co-occurrence matrix (GLCM) can result in very high dimensional feature spaces. Although this high dimensional texture feature space can potentially provide relevant class-specific information for classification, it often also results in over-dimensionality and ill-conditioned statistical formulations. In this work, we propose a polarization channel based feature grouping followed by a multi-classifier decision fusion (MCDF) framework for a levee health monitoring system that seeks to detect landslides in earthen levees. In this system, texture features derived from the SAR imagery are partitioned into small groups according to different polarization channels. A multi-classifier system is then applied to each group to perform classification at the subspace level (i.e., a dedicated classifier for every subspace). Finally, a decision fusion system is employed to fuse decisions generated by each classifier to make a final classification decision (healthy levee versus landslide in this work). The resulting system can handle the high dimensionality of the problem very effectively, and only needs a few training samples for training and optimization.

On an Enhanced PERSIANN-CCS Algorithm for Precipitation Estimation

AbstractBy employing wavelet and selected features (WSF), median merging (MM), and selected curve-fitting (SCF) techniques, the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) has been improved. The PERSIANN-CCS methodology includes the following four main steps: 1) segmentation of satellite cloud images into cloud patches, 2) feature extraction, 3) classification of cloud patches, and 4) derivation of the temperature–rain-rate (T–R) relationship for every cluster. The enhancements help improve step 2 by employing WSF, and step 4 by employing MM and SCF. For the study area herein, the results show that the enhanced methodology improves the equitable threat score (ETS) of the daily and hourly rainfall estimates mostly in the winter and fall. The ETS percentage improvement is about 20% for the daily (10% for hourly) estimates in the winter, 10% for the daily (8% for hourly) estimates in the fall, and at most 5% for the dail...

Pharmacokinetic Tumor Heterogeneity as a Prognostic Biomarker for Classifying Breast Cancer Recurrence Risk.

Goal: Heterogeneity in cancer can affect response to therapy and patient prognosis. Histologic measures have classically been used to measure heterogeneity, although a reliable noninvasive measurement is needed both to establish baseline risk of recurrence and monitor response to treatment. Here, we propose using spatiotemporal wavelet kinetic features from dynamic contrast-enhanced magnetic resonance imaging to quantify intratumor heterogeneity in breast cancer. Methods: Tumor pixels are first partitioned into homogeneous subregions using pharmacokinetic measures. Heterogeneity wavelet kinetic (HetWave) features are then extracted from these partitions to obtain spatiotemporal patterns of the wavelet coefficients and the contrast agent uptake. The HetWave features are evaluated in terms of their prognostic value using a logistic regression classifier with genetic algorithm wrapper-based feature selection to classify breast cancer recurrence risk as determined by a validated gene expression assay. Results: Receiver operating characteristic analysis and area under the curve (AUC) are computed to assess classifier performance using leave-one-out cross validation. The HetWave features outperform other commonly used features (AUC = 0.88 HetWave versus 0.70 standard features). The combination of HetWave and standard features further increases classifier performance (AUCs 0.94). Conclusion: The rate of the spatial frequency pattern over the pharmacokinetic partitions can provide valuable prognostic information. Significance: HetWave could be a powerful feature extraction approach for characterizing tumor heterogeneity, providing valuable prognostic information.

Genetic algorithms and Linear Discriminant Analysis based dimensionality reduction for remotely sensed image analysis

Remotely sensed data (such as hyperspectral imagery) is typically associated with a large number of features, which makes classification challenging. Feature subset selection is an effective approach to alleviate the curse of dimensionality when the number of features contained in datasets is huge. Considering the merits of genetic algorithms (GA) in solving combinatorial problems, GA is becoming an increasingly popular tool for feature subset selection. Most algorithms presented in the literature using GA for feature subset selection use the training classification accuracy of a specific algorithm as the fitness function to optimize over the space of possible feature subsets. Such algorithms require a large amount of time to search for an optimal feature subset. In this paper, we will present a new approach called Genetic Algorithm based Linear Discriminant Analysis (GA-LDA) to extract features in which feature selection and feature extraction are performed simultaneously to alleviate over-dimensionality and result in a useful and robust feature space. Experimental results with classification tasks involving both hyperspectral imagery and SAR data indicate that GA-LDA can result in very low-dimensional feature subspaces yielding high classification accuracies.

Levee anomaly detection using polarimetric synthetic aperture radar data

This research presents results of applying the NASA JPLs Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) quad-polarized L-band data to detect anomalies on earthen levees. Two types of problems / anomalies that occur along these levees which can be precursors to complete failure during a high water event are slough slides and sand boils. The study area encompasses a portion of levees of the lower Mississippi river in the United States. Supervised and unsupervised classification techniques have been employed to detect slough slides along the levee. RX detector, a training-free classification scheme is introduced to detect anomalies on the levee and the results are compared with the k-means clustering algorithm. Using the available ground truth data, a supervised kernel based classification technique using a Support Vector Machine (SVM) is applied for binary classification of slides on the levee versus the healthy levee and the performance is compared with a neural network classifier.

Detection of slump slides on earthen levees using polarimetric SAR imagery

Key results are presented of an extensive project studying the use of synthetic aperture radar (SAR) as an aid to the levee screening process. SAR sensors used are: (1) The NASA UAVSAR (Uninhabited Aerial Vehicle SAR), a fully polarimetric L-band SAR capable of sub-meter ground sample distance; and (2) The German TerraSAR-X radar satellite, also multi-polarized and featuring 1-meter GSD, but using an X-band carrier. The study area is a stretch of 230 km of levees along the lower Mississippi River. The L-band measurements can penetrate vegetation and soil somewhat, thus carrying some information on soil texture and moisture which are relevant features to identifying levee vulnerability to slump slides. While X-band does not penetrate as much, its ready availability via satellite makes multitemporal algorithms practical. Various feature types and classification algorithms were applied to the polarimetry data in the project; this paper reports the results of using the Support Vector Machine (SVM) and back-propagation Artificial Neural Network (ANN) classifiers with a combination of the polarimetric backscatter magnitudes and texture features based on the wavelet transform. Ground reference data used to assess classifier performance is based on soil moisture measurements, soil sample tests, and on site visual inspections.

On the Use of the Genetic Algorithm Filter-Based Feature Selection Technique for Satellite Precipitation Estimation

A feature selection technique is used to enhance the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched by wavelet features. The feature selection technique includes a feature similarity selection method and a filter-based feature selection using genetic algorithm (FFSGA). It is employed in this study to find an optimal set of features where redundant and irrelevant features are removed. The entropy index fitness function is used to evaluate the feature subsets. The results show that using the feature selection technique not only improves the equitable threat score by almost 7% at some threshold values for the winter season, but also it extremely decreases the dimensionality. The bias also decreases in both the winter (January and February) and summer (June, July, and August) seasons.

Use of remote sensing to screen earthen levees

Multi-polarized L-band Synthetic Aperture Radar is investigated for its potential to screen earthen levees for weak points. Various feature detection and classification algorithms are tested for this application, including both radiometric and textural methods such as grey-level co-occurrence matrix and wavelet features.

On the Use of a Cluster Ensemble Cloud Classification Technique in Satellite Precipitation Estimation

In this paper, the link-based cluster ensemble (LCE) method is utilized to improve cloud classification and satellite precipitation estimation. High resolution Satellite Precipitation Estimation (SPE) is based on the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. This modified SPE with the incorporation of LCE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering cloud patches using LCE; and 4) dynamic application of brightness temperature (Tb) and rain-rate relationships, derived using satellite observations. In order to cluster the cloud patches, the LCE method combines multiple data partitions from different clustering methods. The results show that using the cluster ensemble increases the performance of rainfall estimates compared to the SPE algorithm using a Self Organizing Map (SOM) neural network. The false alarm ratio (FAR), probabilities of detection (POD), equitable threat score (ETS), and bias are used as quantitative measures to assess the performance of the algorithm. It is shown that both the ETS and bias provide improvement in the summer and winter seasons. Almost 5% ETS improvement is obtained at some threshold values for the winter season using the cluster ensemble.

Heterogeneity Wavelet Kinetics from DCE-MRI for Classifying Gene Expression Based Breast Cancer Recurrence Risk

Breast tumors are heterogeneous lesions. Intra-tumor heterogeneity presents a major challenge for cancer diagnosis and treatment. Few studies have worked on capturing tumor heterogeneity from imaging. Most studies to date consider aggregate measures for tumor characterization. In this work we capture tumor heterogeneity by partitioning tumor pixels into subregions and extracting heterogeneity wavelet kinetic (HetWave) features from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to obtain the spatiotemporal patterns of the wavelet coefficients and contrast agent uptake from each partition. Using a genetic algorithm for feature selection, and a logistic regression classifier with leave one-out cross validation, we tested our proposed HetWave features for the task of classifying breast cancer recurrence risk. The classifier based on our features gave an ROC AUC of 0.78, outperforming previously proposed kinetic, texture, and spatial enhancement variance features which give AUCs of 0.69, 0.64, and 0.65, respectively.