Detecting the Adjacency Effect in Hyperspectral Imagery With Spectral Unmixing Techniques

作者：Burazerovic Dzevdet^*; Heylen Rob; Geens Bert; Sterckx Sindy; Scheunders Paul

来源：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(3): 1070-1078.

DOI：10.1109/JSTARS.2013.2240656

• Adjacency effect
• image processing
• linear unmixing
• nonlinear unmixing
• remote sensing
• spectral unmixing

The adjacency effect is an interesting phenomenon characterized by the occurrence of path interferences between the reflectances coming from different ground-cover materials. The effect is caused by atmospheric scattering, hence a typical approach to its detection has been the modeling of radiation transfer and spectral correspondence at particular wavelengths. In this paper, we investigate the detection of adjacency effects as being a general unmixing problem. This means that we opt to use spectral unmixing to separate the true signature of a pixel from the background scatter reflected from its adjacent neighborhood. To account for different types of atmospheric scattering, we consider several unmixing methods. These include the established linear-and a recently studied generalized bilinear model, as well as a more data-driven unmixing that could implicitly address nonlinearities not covered by the first mentioned approaches. We evaluate these unmixing models by comparing their results with those obtained from a specialized treatment of the adjacency effect in turbid waters surrounded by vegetated land. This comparison is demonstrated on real data acquired under varying atmospheric conditions.

• 出版日期2013-6