A review of remote sensing image classification techniques: The role of spatio-contextual information
This paper reviewed major remote sensing image classification techniques, including pixel-
wise, sub-pixel-wise, and object-based image classification methods, and highlighted the …
wise, sub-pixel-wise, and object-based image classification methods, and highlighted the …
The application of artificial neural networks to the analysis of remotely sensed data
Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely
sensed data. Although significant progress has been made in image classification based …
sensed data. Although significant progress has been made in image classification based …
Review and evaluation of deep learning architectures for efficient land cover mapping with UAS hyper-spatial imagery: A case study over a wetland
Deep learning has already been proved as a powerful state-of-the-art technique for many
image understanding tasks in computer vision and other applications including remote …
image understanding tasks in computer vision and other applications including remote …
Land use/land cover and land surface temperature analysis in Wayanad district, India, using satellite imagery
ABSTRACT This paper assesses Land Use/Land Cover (LULC) classification and Land
Surface Temperature (LST) in Wayanad district during the years 2004 and 2018. The LULC …
Surface Temperature (LST) in Wayanad district during the years 2004 and 2018. The LULC …
Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes
GM Foody - International Journal of Remote Sensing, 2004 - Taylor & Francis
The absence of assumptions about the dataset to be classified is one of the major attractions
of neural networks for supervised image classification applications. Classification by a …
of neural networks for supervised image classification applications. Classification by a …
Superpixel consistency saliency map generation for weakly supervised semantic segmentation of remote sensing images
The weakly supervised semantic segmentation (WSSS) method aims to assign semantic
labels to each image pixel from weak (image-level) instead of strong (pixel-level) labels …
labels to each image pixel from weak (image-level) instead of strong (pixel-level) labels …
Deep support vector machine for PolSAR image classification
The main problem posed by Polarimetric Synthetic Aperture Radar (PolSAR) image
classification in remote sensing is the ability to develop classifiers that can substantially …
classification in remote sensing is the ability to develop classifiers that can substantially …
[PDF][PDF] 森林类型遥感分类及变化监测研究进展
颜伟， 周雯， 易利龙， 田昕 - 遥感技术与应用, 2019 - rsta.ac.cn
森林是陆地生态系统最主要的植被类型, 利用遥感技术对森林类型分类识别和动态监测对于全球
碳循环研究和森林资源可持续发展具有重要意义. 梳理了森林遥感分类的主要经典方法 …
碳循环研究和森林资源可持续发展具有重要意义. 梳理了森林遥感分类的主要经典方法 …
Deep learning techniques for remote sensing image scene classification: A comprehensive review, current challenges, and future directions
Since last decade, deep learning has made exceptional progress in various fields of artificial
intelligence including image and voice recognition, natural language processing. Inspired …
intelligence including image and voice recognition, natural language processing. Inspired …
A fully complex-valued radial basis function network and its learning algorithm
In this paper, a fully complex-valued radial basis function (FC-RBF) network with a fully
complex-valued activation function has been proposed, and its complex-valued gradient …
complex-valued activation function has been proposed, and its complex-valued gradient …