Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Qianbo Sang"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 18-31 (2021)
Development of very-high-resolution (VHR) remote sensing imaging platforms have resulted in a requirement for developing refined land cover classification maps for various applications. Therefore, aiming at exploring the accurate boundary and complex
Externí odkaz:
https://doaj.org/article/3b45110774ce49b3b8a5eea5c0a68793
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 5508-5517 (2020)
Land cover classification has achieved significant advances by employing deep convolutional network (ConvNet) based methods. Following the paradigm of learning deep models, land cover classification is modeled as semantic segmentation of very high re
Externí odkaz:
https://doaj.org/article/14208d9f195349b7979ef18403a16e4f
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-18
Object detection is an essential task in computer vision. Recently, several convolution neural network (CNN)-based detectors have achieved a great success in natural scenes. However, for optical remote sensing images with a large scale of view, lower
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 18-31 (2021)
Development of very-high-resolution (VHR) remote sensing imaging platforms have resulted in a requirement for developing refined land cover classification maps for various applications. Therefore, aiming at exploring the accurate boundary and complex
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 17:1057-1061
Deep learning (DL) technique is widely applied in remote sensing (RS) applications because of its outstanding nonlinear feature extraction ability. However, with regard to the issues of large-scale and very high-resolution (VHR) land cover classifica
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 5508-5517 (2020)
Land cover classification has achieved significant advances by employing deep convolutional network (ConvNet) based methods. Following the paradigm of learning deep models, land cover classification is modeled as semantic segmentation of very high re
Publikováno v:
Electronics, Vol 9, Iss 1344, p 1344 (2020)
Electronics
Volume 9
Issue 9
Electronics
Volume 9
Issue 9
Deep Convolutional Neural Network (DCNN)-based image scene classification models play an important role in a wide variety of remote sensing applications and achieve great success. However, the large-scale remote sensing images and the intensive compu