On the benefit of topographic dictionaries for detecting disease symptoms on hyperspectral 3D plant models

We analyze the benefit of using topographic dictionaries for a sparse representation (SR) approach for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Topographic dictionaries are an arranged set of basis elements in which neighbored dictionary elements tend to cause similar activations in the SR approach. In this paper, the dictionary is obtained from samples of a healthy plant and partly build in a topographic way by using hyperspectral as well as geometry information, i.e. depth and inclination. It turns out that hyperspectral signals of leafs show a typical structure depending on depth and inclination and thus, both influences can be disentangled in our approach. Rare signals which do not fit into this model, e.g. leaf veins, are also captured in the dictionary in a non-topographic way. A reconstruction error index is used as indicator, in which disease symptoms can be distinguished from healthy plant regions. The advantage of the presented approach is that full spectral and geometry information is needed only once to built the dictionary, whereas the sparse reconstruction is done solely on hyperspectral information.