Results of training of convolutional neural network for satellite four-channel image segmentation are performed. Input images contain blue, green, red and near-infrared channels. The algorithm was trained to detect buildings and other urban areas. Modification of the U-Net neural network with two encoders was used. The values of Sorensen coefficient and Jaccard index were calculated for 16 different urban regions. © 2019 IEEE
The present work will investigate an Artificial Intelligence (AI) approach in the framework of imag...
Semantic segmentation is a fundamental research in remote sensing image processing. Because of the c...
New challenges in remote sensing require the design of a pixel classification method that...
Results of training a convolutional neural network for the satellite image segmentation are presente...
This article presents research results of two convolutional neural networks for building detection o...
The goal of our research was to develop methods based on convolutional neural networks for automatic...
This article presents research results of a convolution neural network for building detection on hig...
This article presents research results of two convolutional neural networks for building detection o...
The paper proposes an approach to detect discrete objects on images, namely buildings using the U-NE...
Recently, convolutional neural networks have grown in popularity in a variety of fields, such as com...
The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new...
Convolutional neural networks for detection geo-objects on the satellite images from DSTL, Landsat -...
This paper presents the results of textural segmentation of satellite images with spatial resolution...
Факультет радиофизики и компьютерных технологийThis paper discusses the image segmentation methods b...
: We introduce a novel learning algorithm for neural networks, with the major feature of being rapid...
The present work will investigate an Artificial Intelligence (AI) approach in the framework of imag...
Semantic segmentation is a fundamental research in remote sensing image processing. Because of the c...
New challenges in remote sensing require the design of a pixel classification method that...
Results of training a convolutional neural network for the satellite image segmentation are presente...
This article presents research results of two convolutional neural networks for building detection o...
The goal of our research was to develop methods based on convolutional neural networks for automatic...
This article presents research results of a convolution neural network for building detection on hig...
This article presents research results of two convolutional neural networks for building detection o...
The paper proposes an approach to detect discrete objects on images, namely buildings using the U-NE...
Recently, convolutional neural networks have grown in popularity in a variety of fields, such as com...
The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new...
Convolutional neural networks for detection geo-objects on the satellite images from DSTL, Landsat -...
This paper presents the results of textural segmentation of satellite images with spatial resolution...
Факультет радиофизики и компьютерных технологийThis paper discusses the image segmentation methods b...
: We introduce a novel learning algorithm for neural networks, with the major feature of being rapid...
The present work will investigate an Artificial Intelligence (AI) approach in the framework of imag...
Semantic segmentation is a fundamental research in remote sensing image processing. Because of the c...
New challenges in remote sensing require the design of a pixel classification method that...