Decision-Level Fusion of DNN Outputs for Improving Feature Detection Performance on Large-Scale Remote Sensing Image Datasets

Cannaday, Alan B.; Chastain, Raymond L.; Hurt, J. Alex; Davis, C.H.; Scott, Grant; Maltenfort, A. J.

doi:10.1109/bigdata47090.2019.9006502

Cited by 3 publications

(2 citation statements)

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“…Five different feature types were used in [10] for decisionlevel fusion of components objects for improving the detection of construction sites. Here we tested feature types that used the F1 score optimization from [10] and represent the first three feature types listed below. We used the normalized cluster scores from the spatial clustering as an additional feature type.…”

Section: A Data Featuresmentioning

confidence: 99%

“…Three data fusion techniques were tested: 1) Decision Tree: A simple decision tree (see [10]) was used to combine the decisions generated for each component using DTA. However, unlike [10], this study does not use an alpha-cut threshold since this was part of the spatial clustering algorithm. Therefore, the decision tree is simplified to a digital logic OR gate with the DTA decisions as binary inputs.…”

Section: B Decision-level Fusion Techniquesmentioning

confidence: 99%

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Improved Search and Detection of Surface-to-Air Missile Sites Using Spatial Fusion of Component Object Detections from Deep Neural Networks

Cannaday

Davis

Scott

2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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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. First, scores computed from a spatial clustering algorithm are normalized to a reference space so that they are independent of image resolution and DNN input chip size. Then, multi-scale DNN detections from various component objects are fused to improve the detection and retrieval of DNN detections of a larger complex feature. We demonstrate the utility of this approach for broad area search and detection of Surface-to-Air Missile (SAM) sites that have a very low occurrence rate (only 16 sites) over a ∼90,000 km 2 study area in SE China. The results demonstrate that spatial fusion of multiscale component-object DNN detections can reduce the detection error rate of SAM Sites by >85% while still maintaining a 100% recall. The novel spatial fusion approach demonstrated here can be easily extended to a wide variety of other challenging object search and detection problems in large-scale remote sensing image datasets.

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Section: A Data Featuresmentioning

confidence: 99%

Section: B Decision-level Fusion Techniquesmentioning

confidence: 99%