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Found 10 papers, 1 papers with code
Broad Area Search and Detection of Surface-to-Air Missile Sites Using Spatial Fusion of Component Object Detections from Deep Neural Networks
Here we demonstrate how Deep Neural Network (DNN) detections of multiple constitutive or component objects that are part of a larger, more complex, and encompassing feature can be spatially fused to improve the search, detection, and retrieval (ranking) of the larger complex feature.
Introducing Fuzzy Layers for Deep Learning
Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification.
Extending the Morphological Hit-or-Miss Transform to Deep Neural Networks
While most deep learning architectures are built on convolution, alternative foundations like morphology are being explored for purposes like interpretability and its connection to the analysis and processing of geometric structures.
Recognizing Image Objects by Relational Analysis Using Heterogeneous Superpixels and Deep Convolutional Features
This paper presents a novel neural architecture that exploits the superpixel feature space.
Fusion of heterogeneous bands and kernels in hyperspectral image processing
Our approach is different in the respect that it is flexible and it follows a well-studied process of visual clustering in high-dimensional spaces.
Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks
Information fusion is an essential part of numerous engineering systems and biological functions, e. g., human cognition.
Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection
A significant challenge in object detection is accurate identification of an object's position in image space, whereas one algorithm with one set of parameters is usually not enough, and the fusion of multiple algorithms and/or parameters can lead to more robust results.
Measuring Conflict in a Multi-Source Environment as a Normal Measure
The results demonstrate that the proposed measure can represent conflict in a meaningful way similar to what a human might expect and from it we can identify conflict within our sources.
Multi-Sensor Conflict Measurement and Information Fusion
In this work, conflict is defined in terms of how little the output from multiple sensors overlap.
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
Namely, we focus on theories, tools and challenges for the RS community.
