Changes at Multiple Spatial Scales

Change detection on rasterized data is extremely dependent on accurate radiometric and geometric rectification. The development of processing tools able to minimise these requirements has been recognised since the late eighties. In the present paper we present a methodology for detecting changes on multidate satellite images with different radiometric and geometric characteristics via Multiresolution Wavelet Analysis. An area in south-eastern Brazil was chosen as case study. In the last 20 years the site was characterised by an increase of mining activities and deforestation. Landsat TM and MSS images from July 1981, November 1985 and August 1998 were used. The idea is to decompose a set of images into averages (overall pattern) and details images at different resolutions. Image differences due to the effects of spatial misregistration, atmospheric condition and sensor characteristics are depicted across scales. No radiometric rectification was applied to the input images and the spatial misregistration ranged from one to three pixels. To detect deforestation and new mining areas we used details at the third and fourth scales. Deforested areas as well as new mining sites were successfully pinpointed without previous radiometric rectification or threshold definition while differences not related to land cover changes were bypassed. Misregistration effects and small area changes are depicted as fine details. Phenological characteristics, atmospheric effects and differences in sensor calibration are represented at coarser scale levels. Hence, using information from intermediate scale levels one can minimise the problems mentioned above.