scholarly journals LAND USE CLASSIFICATION FROM VHR AERIAL IMAGES USING INVARIANT COLOUR COMPONENTS AND TEXTURE

2016 ◽  
Vol XLI-B7 ◽  
pp. 311-317
Author(s):  
A. Movia ◽  
A. Beinat ◽  
T. Sandri
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Landscape Patterns ◽  
Aerial Images ◽  
Land Use Classification ◽  
Essential Information ◽  
Structuring Element ◽  
Very High ◽  
Effective Contribution

Very high resolution (VHR) aerial images can provide detailed analysis about landscape and environment; nowadays, thanks to the rapid growing airborne data acquisition technology an increasing number of high resolution datasets are freely available. <br><br> In a VHR image the essential information is contained in the red-green-blue colour components (RGB) and in the texture, therefore a preliminary step in image analysis concerns the classification in order to detect pixels having similar characteristics and to group them in distinct classes. Common land use classification approaches use colour at a first stage, followed by texture analysis, particularly for the evaluation of landscape patterns. Unfortunately RGB-based classifications are significantly influenced by image setting, as contrast, saturation, and brightness, and by the presence of shadows in the scene. The classification methods analysed in this work aim to mitigate these effects. The procedures developed considered the use of invariant colour components, image resampling, and the evaluation of a RGB texture parameter for various increasing sizes of a structuring element. <br><br> To identify the most efficient solution, the classification vectors obtained were then processed by a K-means unsupervised classifier using different metrics, and the results were compared with respect to corresponding user supervised classifications. <br><br> The experiments performed and discussed in the paper let us evaluate the effective contribution of texture information, and compare the most suitable vector components and metrics for automatic classification of very high resolution RGB aerial images.

scholarly journals LAND USE CLASSIFICATION FROM VHR AERIAL IMAGES USING INVARIANT COLOUR COMPONENTS AND TEXTURE

2016 ◽  
Vol XLI-B7 ◽  
pp. 311-317 ◽  
Author(s):  
A. Movia ◽  
A. Beinat ◽  
T. Sandri
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Landscape Patterns ◽  
Aerial Images ◽  
Land Use Classification ◽  
Essential Information ◽  
Structuring Element ◽  
Very High ◽  
Effective Contribution

Very high resolution (VHR) aerial images can provide detailed analysis about landscape and environment; nowadays, thanks to the rapid growing airborne data acquisition technology an increasing number of high resolution datasets are freely available. &lt;br&gt;&lt;br&gt; In a VHR image the essential information is contained in the red-green-blue colour components (RGB) and in the texture, therefore a preliminary step in image analysis concerns the classification in order to detect pixels having similar characteristics and to group them in distinct classes. Common land use classification approaches use colour at a first stage, followed by texture analysis, particularly for the evaluation of landscape patterns. Unfortunately RGB-based classifications are significantly influenced by image setting, as contrast, saturation, and brightness, and by the presence of shadows in the scene. The classification methods analysed in this work aim to mitigate these effects. The procedures developed considered the use of invariant colour components, image resampling, and the evaluation of a RGB texture parameter for various increasing sizes of a structuring element. &lt;br&gt;&lt;br&gt; To identify the most efficient solution, the classification vectors obtained were then processed by a K-means unsupervised classifier using different metrics, and the results were compared with respect to corresponding user supervised classifications. &lt;br&gt;&lt;br&gt; The experiments performed and discussed in the paper let us evaluate the effective contribution of texture information, and compare the most suitable vector components and metrics for automatic classification of very high resolution RGB aerial images.

scholarly journals AN ITERATIVE INFERENCE PROCEDURE APPLYING CONDITIONAL RANDOM FIELDS FOR SIMULTANEOUS CLASSIFICATION OF LAND COVER AND LAND USE

2015 ◽  
Vol II-3/W5 ◽  
pp. 369-376 ◽  
Author(s):  
L. Albert ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Main Idea ◽  
Aerial Images ◽  
Land Use Classification ◽  
Inference Procedure ◽  
Cover Layer

Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a <i>land cover layer</i> and a <i>land use layer</i>. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result.

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 Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification

Agriculture ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 416 ◽  
Author(s):  
Pei-Chun Chen ◽  
Yen-Cheng Chiang ◽  
Pei-Yi Weng
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Visible Light ◽  
Agricultural Land ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Surface Model ◽  
Multispectral Images ◽  
Land Use Classification ◽  
Elevation Data

An unmanned aerial vehicle (UAV) was used to capture high-resolution aerial images of crop fields. Software-based image analysis was performed to classify land uses. The purpose was to help relevant agencies use aerial imaging in managing agricultural production. This study involves five townships in the Chianan Plain of Chiayi County, Taiwan. About 100 ha of farmland in each township was selected as a sample area, and a quadcopter and a handheld fixed-wing drone were used to capture visible-light images and multispectral images. The survey was carried out from August to October 2018 and aerial photographs were captured in clear and dry weather. This study used high-resolution images captured from a UAV to classify the uses of agricultural land, and then employed information from multispectral images and elevation data from a digital surface model. The results revealed that visible-light images led to low interpretation accuracy. However, multispectral images and elevation data increased the accuracy rate to nearly 90%. Accordingly, such images and data can effectively enhance the accuracy of land use classification. The technology can reduce costs that are associated with labor and time and can facilitate the establishment of a real-time mapping database.

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