Details
| Original language | English |
|---|---|
| Pages (from-to) | 11-18 |
| Number of pages | 8 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 41 |
| Publication status | Published - 2016 |
| Event | 23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016 - Prague, Czech Republic Duration: 12 Jul 2016 → 19 Jul 2016 |
Abstract
The goal of this paper is to investigate the maximum level of semantic resolution that can be achieved in an automated land use change detection process based on mono-temporal, multi-spectral, high-resolution aerial image data. For this purpose, we perform a step-wise refinement of the land use classes that follows the hierarchical structure of most object catalogues for land use databases. The investigation is based on our previous work for the simultaneous contextual classification of aerial imagery to determine land cover and land use. Land cover is determined at the level of small image segments. Land use classification is applied to objects from the geospatial database. Experiments are carried out on two test areas with different characteristics and are intended to evaluate the step-wise refinement of the land use classes empirically. The experiments show that a semantic resolution of ten classes still delivers acceptable results, where the accuracy of the results depends on the characteristics of the test areas used. Furthermore, we confirm that the incorporation of contextual knowledge, especially in the form of contextual features, is beneficial for land use classification.
Keywords
- Aerial imagery, Contextual classification, Geospatial land use database, Land use classification, Semantic resolution
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
Sustainable Development Goals
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 41, 2016, p. 11-18.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Contextual land use classification
T2 - 23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016
AU - Albert, L.
AU - Rottensteiner, F.
AU - Heipke, C.
PY - 2016
Y1 - 2016
N2 - The goal of this paper is to investigate the maximum level of semantic resolution that can be achieved in an automated land use change detection process based on mono-temporal, multi-spectral, high-resolution aerial image data. For this purpose, we perform a step-wise refinement of the land use classes that follows the hierarchical structure of most object catalogues for land use databases. The investigation is based on our previous work for the simultaneous contextual classification of aerial imagery to determine land cover and land use. Land cover is determined at the level of small image segments. Land use classification is applied to objects from the geospatial database. Experiments are carried out on two test areas with different characteristics and are intended to evaluate the step-wise refinement of the land use classes empirically. The experiments show that a semantic resolution of ten classes still delivers acceptable results, where the accuracy of the results depends on the characteristics of the test areas used. Furthermore, we confirm that the incorporation of contextual knowledge, especially in the form of contextual features, is beneficial for land use classification.
AB - The goal of this paper is to investigate the maximum level of semantic resolution that can be achieved in an automated land use change detection process based on mono-temporal, multi-spectral, high-resolution aerial image data. For this purpose, we perform a step-wise refinement of the land use classes that follows the hierarchical structure of most object catalogues for land use databases. The investigation is based on our previous work for the simultaneous contextual classification of aerial imagery to determine land cover and land use. Land cover is determined at the level of small image segments. Land use classification is applied to objects from the geospatial database. Experiments are carried out on two test areas with different characteristics and are intended to evaluate the step-wise refinement of the land use classes empirically. The experiments show that a semantic resolution of ten classes still delivers acceptable results, where the accuracy of the results depends on the characteristics of the test areas used. Furthermore, we confirm that the incorporation of contextual knowledge, especially in the form of contextual features, is beneficial for land use classification.
KW - Aerial imagery
KW - Contextual classification
KW - Geospatial land use database
KW - Land use classification
KW - Semantic resolution
UR - http://www.scopus.com/inward/record.url?scp=84978127413&partnerID=8YFLogxK
U2 - 10.5194/isprsarchives-XLI-B4-11-2016
DO - 10.5194/isprsarchives-XLI-B4-11-2016
M3 - Conference article
AN - SCOPUS:84978127413
VL - 41
SP - 11
EP - 18
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
Y2 - 12 July 2016 through 19 July 2016
ER -