Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 111-116, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
20 Jun 2016
J. G. Rejas Ayuga1,2, R. Martínez Marín2, M. Marchamalo Sacristán2, J. Bonatti3, and J. C. Ojeda2 1National Institute for Aerospace Technology, INTA. Ctra. de Ajalvir km 4 s/n 28850 Torrejón de Ardoz, Spain
2Department of Engineering and Land Morphology, Technical University of Madrid, UPM. Ramiro de Maeztu, 7, 28040 Madrid, Spain
3Costa Rica University, UCR. Campus UCR 4058 San José, Costa Rica
Keywords: Anomaly Detection (AD), Urban Areas, Hyperspectral, High Resolution Data, DATB Abstract. We have studied the spectral features of reflectance and emissivity in the pattern recognition of urban materials in several single hyperspectral scenes through a comparative analysis of anomaly detection methods and their relationship with city surfaces with the aim to improve information extraction processes. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS sensor and HyMAP and MASTER of two cities, Alcalá de Henares (Spain) and San José (Costa Rica) respectively, have been used.

In this research it is assumed no prior knowledge of the targets, thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by image segmentation. Several experiments on urban scenarios and semi-urban have been designed, analyzing the behaviour of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. A new technique for anomaly detection in hyperspectral data called DATB (Detector of Anomalies from Thermal Background) based on dimensionality reduction by projecting targets with unknown spectral signatures to a background calculated from thermal spectrum wavelengths is presented. First results and their consequences in non-supervised classification and extraction information processes are discussed.

Conference paper (PDF, 1817 KB)

Citation: Rejas Ayuga, J. G., Martínez Marín, R., Marchamalo Sacristán, M., Bonatti, J., and Ojeda, J. C.: HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 111-116,, 2016.

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