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.

scholarly journals Land Use Classification of the Deep Convolutional Neural Network Method Reducing the Loss of Spatial Features

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2792 ◽  
Author(s):  
Xuedong Yao ◽  
Hui Yang ◽  
Yanlan Wu ◽  
Penghai Wu ◽  
Biao Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Land Use ◽  
Convolutional Neural Networks ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Land Use Classification ◽  
Deep Convolutional Neural Networks ◽  
Remote Sensing Imagery ◽  
Spatial Features

Land use classification is a fundamental task of information extraction from remote sensing imagery. Semantic segmentation based on deep convolutional neural networks (DCNNs) has shown outstanding performance in this task. However, these methods are still affected by the loss of spatial features. In this study, we proposed a new network, called the dense-coordconv network (DCCN), to reduce the loss of spatial features and strengthen object boundaries. In this network, the coordconv module is introduced into the improved DenseNet architecture to improve spatial information by putting coordinate information into feature maps. The proposed DCCN achieved an obvious performance in terms of the public ISPRS (International Society for Photogrammetry and Remote Sensing) 2D semantic labeling benchmark dataset. Compared with the results of other deep convolutional neural networks (U-net, SegNet, Deeplab-V3), the results of the DCCN method improved a lot and the OA (overall accuracy) and mean F1 score reached 89.48% and 86.89%, respectively. This indicates that the DCCN method can effectively reduce the loss of spatial features and improve the accuracy of semantic segmentation in high resolution remote sensing imagery.

scholarly journals CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS

2018 ◽  
Vol IV-3 ◽  
pp. 251-258 ◽  
Author(s):  
Chun Yang ◽  
Franz Rottensteiner ◽  
Christian Heipke
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Input Data ◽  
Input Image ◽  
Aerial Images ◽  
Positive Contribution ◽  
Land Use Classification

Land cover describes the physical material of the earth’s surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7 % and 77.4 % can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification.

scholarly journals Corrigendum to “Accurate mapping of Brazil nut trees (Bertholletia excelsa) in Amazonian forests using WorldView-3 satellite images and convolutional neural networks”

2021 ◽  
pp. 101326
Author(s):  
Matheus Pinheiro Ferreira ◽  
Rodolfo Georjute Lotte ◽  
Francisco V. D’Elia ◽  
Christos Stamatopoulos ◽  
Do-Hyung Kim ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Satellite Images ◽  
Brazil Nut ◽  
Bertholletia Excelsa ◽  
Amazonian Forests
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