Search Results for author:
Found 43 papers, 13 papers with code
Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data
In this paper, we propose a Contrastive learning based MultiModal Alignment Network (CoMMANet) to align data from different sensors into a shared and discriminative manifold where class information is preserved.
Histogram Layers for Synthetic Aperture Sonar Imagery
Synthetic aperture sonar (SAS) imagery is crucial for several applications, including target recognition and environmental segmentation.
PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study
By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.
Image-to-Height Domain Translation for Synthetic Aperture Sonar
The low grazing angle of the collection geometry, combined with orientation of the sonar path relative to anisotropic texture, poses a significant challenge for image-alignment and other multi-view scene understanding frameworks.
Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security
Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environment.
Robust Semi-Supervised Classification using GANs with Self-Organizing Maps
The problem of discriminating outliers from inliers while maintaining classification accuracy is referred to here as the DOIC problem.
Possibilistic Fuzzy Local Information C-Means with Automated Feature Selection for Seafloor Segmentation
The chosen features and resulting segmentation from the image will be assessed based on a select quantitative clustering validity criterion and the subset of the features that reach a desired threshold will be used for the segmentation process.
Learnable Adaptive Cosine Estimator (LACE) for Image Classification
We compare our LACE to alternative state-of-the art softmax-based and feature regularization approaches.
RandCrowns: A Quantitative Metric for Imprecisely Labeled Tree Crown Delineation
Tree crown delineation provides key information from remote sensing images for forestry, ecology, and management.
The Weakly-Labeled Rand Index
In this paper, a labeling approach and associated modified version of the Rand index for weakly-labeled data is introduced to address these issues.
Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery
In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) (arXiv:1602. 04938) and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar (SAS) data.
Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification
In order to better represent statistical texture information for remote-sensing image classification, in this paper, we investigate performing joint dimensionality reduction and classification using a novel histogram neural network.
Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM
The proposed approach outperforms other attention map and multiple instance learning methods for localization of root objects in minirhizotron imagery.
Outlier Detection through Null Space Analysis of Neural Networks
Specifically, many systems lack the ability to identify when outliers (e. g., samples that are distinct from and not represented in the training data distribution) are being presented to the system.
Super Resolution for Root Imaging
High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes.
Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image.
Histogram Layers for Texture Analysis
We present a histogram layer for artificial neural networks (ANNs).
Peanut Maturity Classification using Hyperspectral Imagery
The maturity assessment process involves the removal of the exocarp of the hull and visually categorizing the mesocarp color into varying color classes from immature (white, yellow, orange) to mature (brown, and black).
Multi-Target Multiple Instance Learning for Hyperspectral Target Detection
They learn a dictionary of target signatures that optimizes detection against a background using the Adaptive Cosine Estimator (ACE) and Spectral Match Filter (SMF).
Investigation of Initialization Strategies for the Multiple Instance Adaptive Cosine Estimator
Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have long been investigated for subsurface explosive hazard detection.
Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation
In this paper, we compare the segmentation performance of a semi-supervised approach using PFLICM and a supervised method using Possibilistic K-NN.
Comparison of Hand-held WEMI Target Detection Algorithms
Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a number of years in subsurface detection of explosive hazards.
Overcoming Small Minirhizotron Datasets Using Transfer Learning
A key component of automated analysis of plant roots from imagery is the automated pixel-level segmentation of roots from their surrounding soil.
Complex Scene Classification of PolSAR Imagery based on a Self-paced Learning Approach
Hence, we propose a supervised classification method aimed at constructing a classifier based on self-paced learning (SPL).
Root Identification in Minirhizotron Imagery with Multiple Instance Learning
In this paper, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed.
Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty
It is valuable to fuse outputs from multiple sensors to boost overall performance.
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
In classifier (or regression) fusion the aim is to combine the outputs of several algorithms to boost overall performance.
Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection
The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented.
Possibilistic Fuzzy Local Information C-Means for Sonar Image Segmentation
Side-look synthetic aperture sonar (SAS) can produce very high quality images of the sea-floor.
Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms
A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor.
Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation
A semi-supervised Partial Membership Latent Dirichlet Allocation approach is developed for hyperspectral unmixing and endmember estimation while accounting for spectral variability and spatial information.
Multiple Instance Hybrid Estimator for Learning Target Signatures
Both simulated and real hyperspectral target detection experiments are shown to illustrate the effectiveness of the method.
Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps
A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced.
Partial Membership Latent Dirichlet Allocation
Topic models (e. g., pLSA, LDA, sLDA) have been widely used for segmenting imagery.
Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation
The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for hyperspectral endmember estimation and spectral unmixing is presented.
Multiple Instance Hyperspectral Target Characterization
In this paper, two methods for multiple instance target characterization, MI-SMF and MI-ACE, are presented.
Heart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances
The goal of this approach is to learn a personalized heartbeat "concept" for an individual.
Instance Influence Estimation for Hyperspectral Target Signature Characterization using Extended Functions of Multiple Instances
From these imprecise labels, eFUMI has been shown to be effective at estimating target signatures in hyperspectral subpixel target detection problems.
Adaptive coherence estimator (ACE) for explosive hazard detection using wideband electromagnetic induction (WEMI)
The adaptive coherence estimator (ACE) estimates the squared cosine of the angle between a known target vector and a sample vector in a whitened coordinate space.
Buried object detection using handheld WEMI with task-driven extended functions of multiple instances
The new algorithm, Task Driven Extended Functions of Multiple Instances, can overcome data that does not have very precise point-wise labels and still learn a highly discriminative dictionary.
Partial Membership Latent Dirichlet Allocation
Topic models (e. g., pLSA, LDA, SLDA) have been widely used for segmenting imagery.
Multiple Instance Dictionary Learning using Functions of Multiple Instances
A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels.
Estimating Target Signatures with Diverse Density
Hyperspectral target detection algorithms rely on knowing the desired target signature in advance.
