Artificial Neural Networks and Evolutionary Computation in Remote Sensing

• Eará dahkkit: Kavzoglu, Taskin (Doaimmaheaddji)
• Materiálatiipa: Elektrovnnalaš Girjji oassi
• Giella: eaŋgalasgiella
• Almmustuhtton: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Fáttát: Research & information: general; convolutional neural network; image segmentation; multi-scale feature fusion; semantic features; Gaofen 6; aerial images; land-use; Tai'an; convolutional neural networks (CNNs); feature fusion; ship detection; optical remote sensing images; end-to-end detection; transfer learning; remote sensing; single shot multi-box detector (SSD); You Look Only Once-v3 (YOLO-v3); Faster RCNN; statistical features; Gaofen-2 imagery; winter wheat; post-processing; spatial distribution; Feicheng; China; light detection and ranging; LiDAR; deep learning; convolutional neural networks; CNNs; mask regional-convolutional neural networks; mask R-CNN; digital terrain analysis; resource extraction; hyperspectral image classification; few-shot learning; quadruplet loss; dense network; dilated convolutional network; artificial neural networks; classification; superstructure optimization; mixed-inter nonlinear programming; hyperspectral images; super-resolution; SRGAN; model generalization; image downscaling; mixed forest; multi-label segmentation; semantic segmentation; unmanned aerial vehicles; classification ensemble; machine learning; Sentinel-2; geographic information system (GIS); earth observation; on-board; microsat; mission; nanosat; AI on the edge; CNN
• Liŋkkat: DOAB: download the publication; DOAB: description of the publication

Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimens...