Very High Geometrical Resolution Research Articles

Generation of Homogeneous VHR Time Series by Nonparametric Regression of Multisensor Bitemporal Images

The availability of multitemporal images acquired by several very high geometrical resolution (VHR) optical sensors makes it possible to build VHR image time series (TS) of images acquired over the same geographical area with a temporal resolution better than the one achievable when considering a single VHR sensor. However, such TS include images showing different characteristics from the geometrical, radiometrical, and spectral viewpoint. Thus, there is a need of methods for building homogeneous VHR optical TS when using multispectral multisensor images. By focusing on the spectral domain, we propose a method to transform a VHR image into the spectral domain of another image in the same multisensor TS but acquired by a different sensor. To this end, a prediction-based approach relying on a nonparametric regression method is employed to mitigate sensor-dependent spectral differences. The impact of possible changes occurred on the ground is mitigated by training the prediction model on unchanged samples, only. Experimental results obtained on VHR optical multisensor images confirm the effectiveness of the proposed approach.

Supervised change detection in VHR images using contextual information and support vector machines

In this paper we study an effective solution to deal with supervised change detection in very high geometrical resolution (VHR) images. High within-class variance as well as low between-class variance that characterize this kind of imagery make the detection and classification of ground cover transitions a difficult task. In order to achieve high detection accuracy, we propose the inclusion of spatial and contextual information issued from local textural statistics and mathematical morphology. To perform change detection, two architectures, initially developed for medium resolution images, are adapted for VHR: Direct Multi-date Classification and Difference Image Analysis. To cope with the high intra-class variability, we adopted a nonlinear classifier: the Support Vector Machines (SVM). The proposed approaches are successfully evaluated on two series of pansharpened QuickBird images.

A Context-Sensitive Technique Robust to Registration Noise for Change Detection in VHR Multispectral Images

This paper presents an automatic context-sensitive technique robust to registration noise (RN) for change detection (CD) in multitemporal very high geometrical resolution (VHR) remote sensing images. Exploiting the properties of RN in VHR images, the proposed technique analyzes the distribution of the spectral change vectors (SCVs) computed according to the change vector analysis (CVA) in a quantized polar domain. The method studies the SCVs falling into each quantization cell at different resolution levels (scales) to automatically identify the effects of RN in the polar domain. This information is jointly exploited with the spatial context information contained in the neighborhood of each pixel for generating the final CD map. The spatial context information is modeled through the definition of adaptive regions homogeneous both in spatial and temporal domain (parcels). Experimental results obtained on real VHR remote sensing multitemporal images confirm the effectiveness of the proposed technique.