Multiresolution Multimodal Sensor Fusion for Remote Sensing Data With Label Uncertainty
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 …
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Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels
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 …
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
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 …
Multiple Instance Choquet Integral For MultiResolution Sensor Fusion
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 …
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Technical Report: Scene Label Ground Truth Map for MUUFL Gulfport Data Set
Abstract This report presents the documentation of the ground truth map for MUUFL Gulfport data set campus 1 scene, provided by manually labeling the pixels in the scene into trees, mostly-grass ground surface, mixed ground surface, dirt and sand, road, water, buildings, shadow of buildings, sidewalk, yellow curb, cloth panels (targets), and unlabeled points. This …
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Spatial and Spectral Unmixing Using the Beta Compositional Model
Abstract This paper introduces the beta compositional model (BCM) for hyperspectral unmixing and four algorithms for unmixing given the BCM. Hyperspectral unmixing estimates the proportion of each endmember at every pixel of a hyperspectral image. Under the BCM, each endmember is a random variable distributed according to a beta distribution. By using a beta distribution, …
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Accounting for Spectral Variability in Hyperspectral Unmixing Using Beta Endmember Distribution
Abstract Hyperspectral imaging is widely used in the field of remote sensing (Goetz, et al., 1985; Green, et al., 1998). In a hyperspectral imaging system, sensors collect radiance/reflectance values over an area (or a scene) across hundreds of spectral bands (Goetz, et al., 1985). The hyperspectral image yielded by such system can be represented by …