Abstract
This paper proposes an approach for the detection of changes in multitemporal Very High Resolution (VHR) optical images acquired by different multispectral sensors. The proposed approach, which is inspired by a recent framework developed to support the design of change-detection systems for single-sensor VHR remote sensing images, addresses and integrates in the general approach a strategy to effectively deal with multisensor information, i.e., to perform change detection between VHR images acquired by different multispectral sensors on two dates. This is achieved by the definition of procedures for the homogenization of radiometric, spectral and geometric image properties. These procedures map images into a common feature space where the information acquired by different multispectral sensors becomes comparable across time. Although the approach is general, here we optimize it for the detection of changes in vegetation and urban areas by employing features based on linear transformations (Tasseled Caps and Orthogonal Equations), which are shown to be effective for representing the multisensor information in a homogeneous physical way irrespectively of the considered sensor. Experiments on multitemporal images acquired by different VHR satellite systems (i.e., QuickBird, WorldView-2 and GeoEye-1) confirm the effectiveness of the proposed approach.
Highlights
The use of Remote Sensing (RS) in the analysis and evaluation of environmental processes evolution is a valuable tool whose relevance has increased in conjunction with the use of digital image processing techniques
The main steps of the proposed approach are: (i) mitigation of differences induced by the use of Very High spatial Resolution (VHR) multitemporal images acquired by different sensors; and (ii) detection of multiple changes occurring on the ground by means of high level physical features
In order to assess the effectiveness of the ΩSys mitigation and the ΩGrd detection approach based on higher-level physical features, Change Vector Analysis (CVA) was applied by considering the 3D feature space defined above and by means of Equations (2)–(5)
Summary
The use of Remote Sensing (RS) in the analysis and evaluation of environmental processes evolution is a valuable tool whose relevance has increased in conjunction with the use of digital image processing techniques. When considering VHR satellite systems, it is difficult to define Time Series (TS) made of images from one single sensor that satisfy the application temporal resolution constraints and show homogeneous acquisition conditions characteristics (e.g., similar light conditions, similar acquisition angle). This is mainly due to the satellite revisit period, the possible competing orders of different users on the satellite pointing, the limited life of a satellite mission, and weather conditions. Since a considerable number of satellites have been launched in the last decades, the above-mentioned limitations can be mitigated by building TS where images acquired by different multispectral VHR sensors are considered
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