Bi-Capacity Choquet Integral for Sensor Fusion with Label Uncertainty

by Conference PaperPosted on No Comments

Abstract Sensor fusion combines data from multiple sensor sources to improve reliability, robustness, and accuracy of data interpretation. The Fuzzy Integral (FI), in particular, the Choquet integral (ChI), is often used as a powerful nonlinear aggregator for fusion across multiple sensors. However, existing supervised ChI learning algorithms typically require precise training labels for each input …

Read more “Bi-Capacity Choquet Integral for Sensor Fusion with Label Uncertainty”

Multiresolution Multimodal Sensor Fusion for Remote Sensing Data With Label Uncertainty

by Journal PaperPosted on No Comments

Abstract In remote sensing, each sensor can provide complementary or reinforcing information. It is valuable to fuse outputs from multiple sensors to boost overall performance. Previous supervised fusion methods often require accurate labels for each pixel in the training data. However, in many remote sensing applications, pixel-level labels are difficult or infeasible to obtain. In …

Read more “Multiresolution Multimodal Sensor Fusion for Remote Sensing Data With Label Uncertainty”

Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels

by Conference PaperPosted on No Comments

Abstract Classifier fusion methods integrate complementary information from multiple classifiers or detectors and can aid remote sensing applications such as target detection and hyperspectral image analysis. The Choquet integral (CI), parameterized by fuzzy measures (FMs), has been widely used in the literature as an effective non-linear fusion framework. Standard supervised CI fusion algorithms often require …

Read more “Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels”

Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications

by Journal PaperPosted on No Comments

Abstract In classifier (or regression) fusion, the aim is to combine the outputs of several algorithms to boost overall performance. Standard supervised fusion algorithms often require accurate and precise training labels. However, accurate labels may be difficult to obtain in many remote sensing applications. This paper proposes novel classification and regression fusion models that can …

Read more “Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications”

Multiple Instance Choquet Integral For MultiResolution Sensor Fusion

by ThesisPosted on No Comments

Abstract Imagine you are traveling to Columbia,MO for the first time. On your flight to Columbia, the woman sitting next to you recommended a bakery by a large park with a big yellow umbrella outside. After you land, you need directions to the hotel from the airport. Suppose you are driving a rental car, you …

Read more “Multiple Instance Choquet Integral For MultiResolution Sensor Fusion”

Environmentally-Adaptive Target Recognition for SAS Imagery

by Conference PaperPosted on No Comments

Abstract Characteristics of underwater targets displayed in synthetic aperture sonar (SAS) imagery vary depending on their environmental context. Discriminative features in sea grass may differ from the features that are discriminative in sand ripple, for example. Environmentally-adaptive target detection and classification systems that take into account environmental context, therefore, have the potential for improved results. …

Read more “Environmentally-Adaptive Target Recognition for SAS Imagery”

Multiple-instance Learning-based Sonar Image Classification

by Conference PaperPosted on No Comments

Abstract An approach to image labeling by seabed context based on multiple-instance learning via embedded instance selection (MILES) is presented. Sonar images are first segmented into superpixels with associated intensity and texture feature distributions. These superpixels are defined as the “instances” and the sonar images are defined as the “bags” within the MILES classification framework. …

Read more “Multiple-instance Learning-based Sonar Image Classification”

Multiple Instance Choquet Integral for Classifier Fusion

by Conference PaperPosted on No Comments

Abstract The Multiple Instance Choquet integral (MICI) for classifier fusion and an evolutionary algorithm for parameter estimation is presented. The Choquet integral has a long history of providing an effective framework for non-linear fusion. Previous methods to learn an appropriate measure for the Choquet integral assumed accurate and precise training labels (with low levels of …

Read more “Multiple Instance Choquet Integral for Classifier Fusion”

Possibilistic context identification for SAS imagery

by Conference PaperPosted on No Comments

Abstract This paper proposes a possibilistic context identification approach for synthetic aperture sonar (SAS) imagery. SAS seabed imagery can display a variety of textures that can be used to identify seabed types such as sea grass, sand ripple and hard-packed sand, etc. Target objects in SAS imagery often have varying characteristics and features due to …

Read more “Possibilistic context identification for SAS imagery”