Details

Autor(en) / Beteiligte

• Scott, Grant J.
• Hagan, Kyle C.
• Marcum, Richard A.
• Hurt, James Alex
• Anderson, Derek T.
• Davis, Curt H.

Titel

Enhanced Fusion of Deep Neural Networks for Classification of Benchmark High-Resolution Image Data Sets

Ist Teil von

• IEEE geoscience and remote sensing letters, 2018-09, Vol.15 (9), p.1451-1455

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2018

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Accurate land cover classification and detection of objects in high-resolution electro-optical remote sensing imagery (RSI) have long been a challenging task. Recently, important new benchmark data sets have been released which are suitable for land cover classification and object detection research. Here, we present state-of-the-art results for four benchmark data sets using a variety of deep convolutional neural networks (DCNN) and multiple network fusion techniques. We achieve 99.70%, 99.66%, 97.74%, and 97.30% classification accuracies on the PatternNet, RSI-CB256, aerial image, and RESISC-45 data sets, respectively, using the Choquet integral with a novel data-driven optimization method presented in this letter. The relative reduction in classification errors achieved by this data driven optimization is 25%-45% compared with the single best DCNN results.