Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifyi...
This study compares some different types of spectral domain transformations for convolutional neural...
In this paper we address the challenge of land cover classification for satellite images via Deep Le...
Classifying the remote sensing images requires a deeper understanding of remote sensing imagery, mac...
In using the convolutional neural network (CNN) for classification, there is a set of hyperparameter...
Land cover describes the physical material of the earth’s surface, whereas land use describes the so...
Land cover describes the physical material of the earth's surface, whereas land use describes the so...
Identifying the physical aspect of the earth’s surface (Land cover) and also how we exploit the land...
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many env...
Land use and land cover are two important variables in remote sensing. Commonly, the information of ...
Semantic land cover classification of satellite images or airborne images is becoming increasingly i...
© 2019 by the authors. In recent years, remote sensing researchers have investigated the use of diff...
International audienceRecent Convolutional Neural Network (CNN) has shown great potential in image c...
Land cover and its dynamic information is the basis for characterizing surface conditions, supportin...
Classification of aerial photographs relying purely on spectral content is a challenging topic in re...
The paper describes the process of training a convolutional neural network to segment land into its ...
This study compares some different types of spectral domain transformations for convolutional neural...
In this paper we address the challenge of land cover classification for satellite images via Deep Le...
Classifying the remote sensing images requires a deeper understanding of remote sensing imagery, mac...
In using the convolutional neural network (CNN) for classification, there is a set of hyperparameter...
Land cover describes the physical material of the earth’s surface, whereas land use describes the so...
Land cover describes the physical material of the earth's surface, whereas land use describes the so...
Identifying the physical aspect of the earth’s surface (Land cover) and also how we exploit the land...
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many env...
Land use and land cover are two important variables in remote sensing. Commonly, the information of ...
Semantic land cover classification of satellite images or airborne images is becoming increasingly i...
© 2019 by the authors. In recent years, remote sensing researchers have investigated the use of diff...
International audienceRecent Convolutional Neural Network (CNN) has shown great potential in image c...
Land cover and its dynamic information is the basis for characterizing surface conditions, supportin...
Classification of aerial photographs relying purely on spectral content is a challenging topic in re...
The paper describes the process of training a convolutional neural network to segment land into its ...
This study compares some different types of spectral domain transformations for convolutional neural...
In this paper we address the challenge of land cover classification for satellite images via Deep Le...
Classifying the remote sensing images requires a deeper understanding of remote sensing imagery, mac...