Very High Resolution Multiangle Urban Classification Analysis

The high-performance camera control systems carried aboard the DigitalGlobe WorldView satellites, WorldView-1 and WorldView-2, are capable of rapid retargeting and high off-nadir imagery collection. This provides the capability to collect dozens of multiangle very high spatial resolution images over a large target area during a single overflight. In addition, WorldView-2 collects eight bands of multispectral data. This paper discusses the improvements in urban classification accuracy available through utilization of the spatial and spectral information from a WorldView-2 multiangle image sequence collected over Atlanta, GA, in December 2009. Specifically, the implications of adding height data and multiangle multispectral reflectance, both derived from the multiangle sequence, to the textural, morphological, and spectral information of a single WorldView-2 image are investigated. The results show an improvement in classification accuracy of 27% and 14% for the spatial and spectral experiments, respectively. Additionally, the multiangle data set allows the differentiation of classes not typically well identified by a single image, such as skyscrapers and bridges as well as flat and pitched roofs.

[1]  José Crespo,et al.  Theoretical aspects of morphological filters by reconstruction , 1995, Signal Process..

[2]  J. L. Privette,et al.  An efficient strategy for the inversion of bidirectional reflectance models with satellite remote sensing data , 1994 .

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

[4]  Florence Tupin,et al.  Building extraction and 3D reconstruction in urban areas from high-resolution optical and SAR imagery , 2009, 2009 Joint Urban Remote Sensing Event.

[5]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[6]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[7]  A. Strahler,et al.  Classification of ASAS multiangle and multispectral measurements using artificial neural networks , 1996 .

[8]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[9]  Fabio Del Frate,et al.  Hyperspectral and Multiangle CHRIS–PROBA Images for the Generation of Land Cover Maps , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Bor-Chen Kuo,et al.  Improved statistics estimation and feature extraction for hyperspectral data classification , 2001 .

[11]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[12]  M. Pal,et al.  Random forests for land cover classification , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[13]  Paul M. Mather,et al.  Computer Processing of Remotely-Sensed Images: An Introduction , 1988 .

[14]  Debra P. C. Peters,et al.  Support vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery , 2007 .

[15]  S. K. Jenson,et al.  Extracting topographic structure from digital elevation data for geographic information-system analysis , 1988 .

[16]  R. Lacaze,et al.  Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption , 2003 .

[17]  Mark T. Keane,et al.  Geometric Proportional Analogies In Topographic Maps: Theory and Application , 2005, SGAI Conf..

[18]  D. Montgomery,et al.  Digital elevation model grid size, landscape representation, and hydrologic simulations , 1994 .

[19]  J. Brasington,et al.  Object-based land cover classification using airborne LiDAR , 2008 .

[20]  David A. Landgrebe,et al.  Signal Theory Methods in Multispectral Remote Sensing , 2003 .

[21]  Claus Brenner,et al.  Extraction of buildings and trees in urban environments , 1999 .

[22]  John R. Schott,et al.  Remote Sensing: The Image Chain Approach , 1996 .

[23]  W. Verhoef,et al.  Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models , 2003 .

[24]  Gail P. Anderson,et al.  MODTRAN4 radiative transfer modeling for atmospheric correction , 1999, Optics & Photonics.

[25]  E. M. Wright,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[26]  C. Small.  High spatial resolution spectral mixture analysis of urban reflectance , 2003 .

[27]  San Diego,et al.  New Approaches to Generating and Processing High Resolution Elevation Data with Imagery , 2007 .

[28]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[29]  Gary A. Peterson,et al.  Soil Attribute Prediction Using Terrain Analysis , 1993 .

[30]  William J. Emery,et al.  A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification , 2009 .

[31]  G. Foody.  Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy , 2004 .

[32]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[33]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[34]  L. Band.  Topographic Partition of Watersheds with Digital Elevation Models , 1986 .

[35]  J Richards,et al.  Computer processing of remotely-sensed images: An introduction , 1990 .

[36]  Frederic Teston,et al.  The PROBA/CHRIS mission: a low-cost smallsat for hyperspectral multiangle observations of the Earth surface and atmosphere , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Paul E. Lewis,et al.  FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[38]  Gu Haiyan,et al.  Fusion of High-resolution Aerial Imagery and Lidar Data for Object-oriented Urban Landcover Classification Based on Svm , 2007 .

[39]  Teemu Hakala,et al.  Measurement of Reflectance Properties of Asphalt Surfaces and Their Usability as Reference Targets for Aerial Photos , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Fabio Del Frate,et al.  Use of CHRIS PROBA images for land use products , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[41]  David Riaño,et al.  Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003) , 2003, IEEE Trans. Geosci. Remote. Sens..

[42]  Bernard Pinty,et al.  Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview , 1998, IEEE Trans. Geosci. Remote. Sens..

[43]  Kiyun Yu,et al.  Assessing the Possibility of Landcover Classification Using Lidar Intensity Data , 2002 .

[44]  Henri Maître,et al.  A Fusion Scheme for Joint Retrieval of Urban Height Map and Classification From High-Resolution Interferometric SAR Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[45]  V. Liesenberg,et al.  Variations in reflectance with seasonality and viewing geometry: Implications for classification of Brazilian savanna physiognomies with MISR/Terra data , 2007 .

[46]  Michael D. King,et al.  BRDF Analysis of Savanna Vegetation and Salt-Pan Samples , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Fabio Pacifici,et al.  On the relative predictive value of the new spectral bands in the WorldWiew-2 sensor , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[48]  J. Muller,et al.  MODIS BRDF / Albedo Product : Algorithm Theoretical Basis Document Version 5 . 0 , 1999 .

[49]  C. Padwick,et al.  WORLDVIEW-2 PAN-SHARPENING , 2010 .

[50]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[51]  William J. Emery,et al.  Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines , 2009, IEEE Transactions on Geoscience and Remote Sensing.