TY  - GEN
T1  - Artificial Neural Networks and Evolutionary Computation in Remote Sensing
A2  - Kavzoglu, Taskin
LA  - eng
PB  - MDPI - Multidisciplinary Digital Publishing Institute
YR  - 2021
UL  - https://ebooks.jgu.edu.in/Record/doab-20.500.12854-68306
AB  - 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 multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.
SN  - 9783039438273
SN  - 9783039438280
KW  - convolutional neural network
KW  - image segmentation
KW  - multi-scale feature fusion
KW  - semantic features
KW  - Gaofen 6
KW  - aerial images
KW  - land-use
KW  - Tai’an
KW  - convolutional neural networks (CNNs)
KW  - feature fusion
KW  - ship detection
KW  - optical remote sensing images
KW  - end-to-end detection
KW  - transfer learning
KW  - remote sensing
KW  - single shot multi-box detector (SSD)
KW  - You Look Only Once-v3 (YOLO-v3)
KW  - Faster RCNN
KW  - statistical features
KW  - Gaofen-2 imagery
KW  - winter wheat
KW  - post-processing
KW  - spatial distribution
KW  - Feicheng
KW  - China
KW  - light detection and ranging
KW  - LiDAR
KW  - deep learning
KW  - convolutional neural networks
KW  - CNNs
KW  - mask regional-convolutional neural networks
KW  - mask R-CNN
KW  - digital terrain analysis
KW  - resource extraction
KW  - hyperspectral image classification
KW  - few-shot learning
KW  - quadruplet loss
KW  - dense network
KW  - dilated convolutional network
KW  - artificial neural networks
KW  - classification
KW  - superstructure optimization
KW  - mixed-inter nonlinear programming
KW  - hyperspectral images
KW  - super-resolution
KW  - SRGAN
KW  - model generalization
KW  - image downscaling
KW  - mixed forest
KW  - multi-label segmentation
KW  - semantic segmentation
KW  - unmanned aerial vehicles
KW  - classification ensemble
KW  - machine learning
KW  - Sentinel-2
KW  - geographic information system (GIS)
KW  - earth observation
KW  - on-board
KW  - microsat
KW  - mission
KW  - nanosat
KW  - AI on the edge
KW  - CNN
KW  - thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
ER  -