This study proposes a light convolutional neural network (LCNN) well-fitted for medium-resolution (30-m) land-cover classification. The LCNN attains high accuracy without overfitting, even with a small number of training samples, and has lower computational costs due to its much lighter design compared to typical convolutional neural networks for high-resolution or hyperspectral image classification tasks. The performance of the LCNN was compared to that of a deep convolutional neural network, support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF). SVM, KNN, and RF were tested with both patch-based and pixel-based systems. Three 30 km × 30 km test sites of the Level II National Land Cover Database were used for refe...
Identifying the physical aspect of the earth’s surface (Land cover) and also how we exploit the land...
There is an emerging interest in using hyperspectral data for land cover classification. The motivat...
Efficiently implementing remote sensing image classification with high spatial resolution imagery ca...
Land cover and its dynamic information is the basis for characterizing surface conditions, supportin...
This study compares some different types of spectral domain transformations for convolutional neural...
Large scale Landsat image classification is the key to acquire national even global land cover map. ...
International audienceDetailed, accurate and frequent land cover mapping is a prerequisite for sever...
In using the convolutional neural network (CNN) for classification, there is a set of hyperparameter...
For land management and planning, information on the Land Use Land Cover (LULC) is vital. In this re...
The paper describes the process of training a convolutional neural network to segment land into its ...
Classification of aerial photographs relying purely on spectral content is a challenging topic in re...
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many env...
Researchers constantly seek more efficient detection techniques to better utilize enhanced image res...
Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (L...
Land cover mapping from satellite images has progressed from visual and statistical approaches to Ra...
Identifying the physical aspect of the earth’s surface (Land cover) and also how we exploit the land...
There is an emerging interest in using hyperspectral data for land cover classification. The motivat...
Efficiently implementing remote sensing image classification with high spatial resolution imagery ca...
Land cover and its dynamic information is the basis for characterizing surface conditions, supportin...
This study compares some different types of spectral domain transformations for convolutional neural...
Large scale Landsat image classification is the key to acquire national even global land cover map. ...
International audienceDetailed, accurate and frequent land cover mapping is a prerequisite for sever...
In using the convolutional neural network (CNN) for classification, there is a set of hyperparameter...
For land management and planning, information on the Land Use Land Cover (LULC) is vital. In this re...
The paper describes the process of training a convolutional neural network to segment land into its ...
Classification of aerial photographs relying purely on spectral content is a challenging topic in re...
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many env...
Researchers constantly seek more efficient detection techniques to better utilize enhanced image res...
Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (L...
Land cover mapping from satellite images has progressed from visual and statistical approaches to Ra...
Identifying the physical aspect of the earth’s surface (Land cover) and also how we exploit the land...
There is an emerging interest in using hyperspectral data for land cover classification. The motivat...
Efficiently implementing remote sensing image classification with high spatial resolution imagery ca...