DMF Data Format Usage for Change Detection

Alejandro Cristo,Pablo J Martínez,David Valencia,Rosa M Pérez

doi:10.1155/2012/341585

Alejandro Cristo, Pablo J Martínez + Show 2 more

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https://doi.org/10.1155/2012/341585

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Abstract

Because of the availability of an overwhelming amount of remote sensing data obtained by different instruments, new techniques and applications have been developed in order to pursue the objective of detecting changes that occur in a particular area of the Earth or that affect a large part of the Earth. These studies have used datasets covering different wavelength ranges (visible, IR, radar, and so on), but common to all of them is the necessity for great accuracy to ensure that no bias is introduced due to data correction. Otherwise, a result may be the generation of false positives. Also, many studies have used several different datasets for the same area to detect changes (this is usually called data fusion), but there exists no specific data structure designed for this purpose. In this paper, we propose a data structure to be used for accurate change detection. This structure is transparent to the user and can be used for data fusion to improve those studies.

Highlights

In the last decades, several remote sensing satellites for quasidaily global Earth observation have been launched, and various new and improved versions of their instruments are to be launched in this present decade
In order to test the performance of the Diffused Matrix Format (DMF) format in application to the field of change detection, a dataset comprising two 63-band images acquired by the AHS sensor [22, 23] was used (Figure 3)
Instituto Nacional de Tecnica Espacial (INTA)’s purpose in acquiring these two real images with different spatial resolutions but with no changes at all was to provide a reliable dataset focused on the development of new methodological approaches to change detection

Summary

Introduction

Several remote sensing satellites for quasidaily global Earth observation have been launched, and various new and improved versions of their instruments are to be launched in this present decade. The data they provide open up great opportunities for the rapidly growing field of change detection of the Earth [1,2,3]. It is clear that is a very interesting area for remote sensing, geology, agriculture, and so forth, and even for politics and local governments Despite having such a great potential, it suffers from acute problems and difficulties. The main difficulty arises when one considers the very basic need to generate a simple product of a change detection algorithm and the particular necessity of having two images of the same scene acquired on different dates, and which reproduce exactly the same area (or at least a sufficiently large common area)

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