Global Elevation Ancillary Data for Land-use Classification Using Granular Neural Networks

2008 ◽  
Vol 74 (1) ◽  
pp. 55-63 ◽  
Author(s):  
Demetris Stathakis ◽  
Ioannis Kanellopoulos
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Land Use Classification ◽  
Ancillary Data ◽  
Granular Neural Networks

Granular neural networks for land use classification

2004 ◽  
Vol 9 (5) ◽  
pp. 332-340 ◽  
Author(s):  
Athanassios Vasilakos ◽  
Demetris Stathakis
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Land Use Classification ◽  
Granular Neural Networks

LAND-USE CLASSIFICATION OF GRAY-SCALE AERIAL IMAGES USING PROBABILISTIC NEURAL NETWORKS

2004 ◽  
Vol 47 (5) ◽  
pp. 1813-1819 ◽  
Author(s):  
D. Ashish ◽  
G. Hoogenboom ◽  
R. W. McClendon
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Aerial Images ◽  
Land Use Classification ◽  
Probabilistic Neural Networks ◽  
Gray Scale

Land-Use Classification Using Convolutional Neural Networks

2021 ◽  
Vol 55 (4) ◽  
pp. 358-367
Author(s):  
A. M. Stepchenko
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Convolutional Neural Networks ◽  
Land Use Classification

scholarly journals Land Classification using Convolutional Neural Networks

2020 ◽  
Vol 9 (2) ◽  
pp. 79-83
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Convolutional Neural Networks ◽  
Satellite Images ◽  
Residential Buildings ◽  
Classification Performance ◽  
Optimization Techniques ◽  
Physical Aspect ◽  
Land Use Classification ◽  
Forest Lands

Identifying the physical aspect of the earth’s surface (Land cover) and also how we exploit the land (Land use) is a challenging problem in environment monitoring and much of other subdomains. One of the most efficient ways to do this is through Remote Sensing (analyzing satellite images). For such classification using satellite images, there exist many algorithms and methods, but they have several problems associated with them, such as improper feature extraction, poor efficiency, etc. Problems associated with established land-use classification methods can be solved by using various optimization techniques with the Convolutional neural networks(CNN). The structure of the Convolutional neural network model is modified to improve the classification performance, and the overfitting phenomenon that may occur during training is avoided by optimizing the training algorithm. This work mainly focuses on classifying land types such as forest lands, bare lands, residential buildings, Rivers, Highways, cultivated lands, etc. The outcome of this work can be further processed for monitoring in various domains.

scholarly journals IMPROVING THE CLASSIFICATION OF LAND USE OBJECTS USING DENSE CONNECTIVITY OF CONVOLUTIONAL NEURAL NETWORKS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 667-673
Author(s):  
A. Gujrathi ◽  
C. Yang ◽  
F. Rottensteiner ◽  
K. M. Buddhiraju ◽  
C. Heipke
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Convolutional Neural Networks ◽  
Spatial Databases ◽  
Aerial Images ◽  
Automatic Process ◽  
Surface Model ◽  
Land Use Classification ◽  
Global Average

Abstract. Land use is an important variable in remote sensing which describes the functions carried out on a piece of land in order to obtain benefits and is especially useful to the personnel working in the fields of urban management and planning. The land use information is maintained by national mapping agencies in geo-spatial databases. Commonly, land use data is stored in the form of polygon objects; the label of the object indicates land use. The main goal of classification of land use objects is to update an existing database in an automatic process. Recently, Convolutional Neural Networks (CNN) have been widely used to tackle this task utilizing high resolution aerial images (and derived data such as digital surface model). One big challenge classifying polygons is to deal with the large variation in their geometrical extent. For this challenge, we adopt the method of Yang et al. (2019) to decompose polygons into regular patches of fixed size. The decomposition leads to two sets of polygons: small and large, where the former suffers from a lower identification rate. In this paper, we propose CNN methods which incorporate dense connectivity and integrate it with intermediate information via global average pooling to improve land use classification, mainly focusing on small polygons. We present different network variants by incorporating intermediate information via global average pooling from different stages of the network. We test our methods on two sites; our experiments show that the dense connectivity and integration of intermediate information has a positive effect not only on the classification accuracy on the whole but also on the identification of small polygons.

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