The TNO Multiband Image Data Collection

By Alexander Toet. Data in Brief, 2017

437 citations recorded by the source corpus.

Despite of the ongoing interest in the fusion of multi-band images for surveillance applications and a steady stream of publications in this area, there is only a very small number of static registered multi-band test images (and a total lack of dynamic image sequences) publicly available for the development and evaluation of image fusion algorithms. To fill this gap, the TNO Multiband Image Collection provides intensified visual (390–700 nm), near-infrared (700–1000 nm), and longwave infrared (8–12 µm) nighttime imagery of different military and surveillance scenarios, showing different objects and targets (e.g., people, vehicles) in a range of different (e.g., rural, urban) backgrounds. The dataset will be useful for the development of static and dynamic image fusion algorithms, color fusion algorithms, multispectral target detection and recognition algorithms, and dim target detection algorithms.

Subfields: Aerospace Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/1d834ece60b68ae9d0d6a6ad1dabb6ca33b25978

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Jinde Cao
2. Shishuang Yu
3. Xue Wang
4. Zheng Guan
5. Ya Li

In Media Technology

1. Risheng Liu
2. Jinyuan Liu
3. Wei Zhong
4. Xin Fan
5. Zhongxuan Luo
6. Jinde Cao
7. Shishuang Yu
8. Xue Wang

Citations in the corpus

• Infrared and Visible Image Fusion via Decoupling Network (2022, methodology): “4) We have evaluated the proposed DNFusion on the TNO dataset [19], FLIR-test dataset [20], near-infrared (NIR) dataset [21], and multiscenario multimodality (M 3 FD) dataset [22].”
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection (2022, background): “In recent years, we have witnessed the rapid evolution of IVIF benchmarks, including the TNO Image Fusion [33], INO Videos Analytics 1 , OSU Color-Thermal 2 , and RoadScene [35] and Multispectral datasets [32].”