Semi-automatic verification of cropland and grassland using very high resolution mono-temporal satellite images

Many public and private decisions rely on geospatial information stored in a GIS database. For good decision making this information has to be complete, consistent, accurate and up-to-date. In this paper we introduce a new approach for the semi-automatic verification of a specific part of the, possibly outdated GIS database, namely cropland and grassland objects, using mono-temporal very high resolution (VHR) multispectral satellite images. The approach consists of two steps: first, a supervised pixel-based classification based on a Markov Random Field is employed to extract image regions which contain agricultural areas (without distinction between cropland and grassland), and these regions are intersected with boundaries of the agricultural objects from the GIS database. Subsequently, GIS objects labelled as cropland or grassland in the database and showing agricultural areas in the image are subdivided into different homogeneous regions by means of image segmentation, followed by a classification of these segments into either cropland or grassland using a Support Vector Machine. The classification result of all segments belonging to one GIS object are finally merged and compared with the GIS database label. The developed approach was tested on a number of images. The evaluation shows that errors in the GIS database can be significantly reduced while also speeding up the whole verification task when compared to a manual process.

[1]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Petra Helmholz.  Verifikation von Ackerland- und Grünlandobjekten eines topographischen Datensatzes mit monotemporalen Bildern , 2012 .

[3]  Christian Heipke,et al.  Multitemporale Luftbildinterpretation: Strategie und Anwendung , 2001, Künstliche Intell..

[4]  Volker Walter,et al.  Object-based classification of remote sensing data for change detection , 2004 .

[5]  F. Rottensteiner,et al.  CONTEXT MODELS FOR CRF-BASED CLASSIFICATION OF MULTITEMPORAL REMOTE SENSING DATA , 2012 .

[6]  Peng Gong,et al.  Vineyard identification in an oak woodland landscape with airborne digital camera imagery , 2003 .

[7]  Timothy A. Warner,et al.  Spatial Classification of Orchards and Vineyards with High Spatial Resolution Panchromatic Imagery , 2005 .

[8]  Wolfgang Förstner,et al.  A Framework for Low Level Feature Extraction , 1994, ECCV.

[9]  Helmholz,et al.  Enhancing the Automatic Verification of Cropland in High-Resolution Satellite Imagery , 2008 .

[10]  Roger Trias-Sanz,et al.  Texture Orientation and Period Estimator for Discriminating Between Forests, Orchards, Vineyards, and Tilled Fields , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Clement Atzberger,et al.  Wavelet-based texture measures for object-based classification of aerial images , 2013 .

[12]  Frédéric Baret,et al.  Vineyard identification and description of spatial crop structure by per-field frequency analysis , 2002 .

[13]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[14]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

[15]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[16]  Torsten Prinz,et al.  JAVA-based Texture Analysis Employing Neighborhood Gray-Tone Difference Matrix (NGTDM) for Optimization of Land Use Classifications in High Resolution Remote Sensing Data , 2009 .

[17]  Bernd Krickel,et al.  Informationserhebung zur Aktualisierung von ATKIS® und Freizeitkataster in Nordrhein-Westfalen , 2010 .

[18]  Christian Heipke,et al.  Image‐based quality assessment of road databases , 2008, Int. J. Geogr. Inf. Sci..

[19]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Jan-Olof Eklundh,et al.  Computer Vision — ECCV '94 , 1994, Lecture Notes in Computer Science.

[21]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[22]  C. Heipke,et al.  Semi-automatic quality control of topographic data sets , 2012 .

[23]  Volker Hochschild,et al.  Multisensoral, object- and GIS-based classification of grassland habitats in the Bio sphere Reserve Schwäbische Alb Multisensorale, objektbasierte und GIS gestützte Klassifizierung von Grünlandbiotoptypen im Biosphärengebiet Schwäbische Alb , 2013 .

[24]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[25]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[26]  Hassiba Nemmour,et al.  Multiple support vector machines for land cover change detection: An application for mapping urban extensions , 2006 .

[27]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..