DETECTION OF DISEASE SYMPTOMS ON HYPERSPECTRAL 3D PLANT MODELS

Ribana Roscher,Jan Dupuis,Heiner Kuhlmann,Jan Behmann,Lutz Plümer,Anne-Katrin Mahlein

doi:10.5194/isprsannals-iii-7-89-2016

Abstract

We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of &lt;i&gt;Cercospora&lt;/i&gt; leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.

Highlights

Hyperspectral images are an important tool for assessing the vitality and stress response of plants (Fiorani et al, 2012; Mahlein et al, 2012; Behmann et al, 2014)
The reconstruction error of the sparse representation approach as well as the decision value obtained by One-Class Support Vector Machines (OCSVM) is higher for pixel with disease symptoms than for healthy pixels
While OCSVM show a high variability within each leaf and only small differences between leafs, the sparse representation approach shows a small variability within a leaf but large differences between leafs

Summary

Introduction

Hyperspectral images are an important tool for assessing the vitality and stress response of plants (Fiorani et al, 2012; Mahlein et al, 2012; Behmann et al, 2014). The identification of disease symptoms using hyperspectral images is an established approach. Due to the unknown statistical distributions of hyperspectral data and disease symptoms, methods from the machine learning domain are used frequently. Applications cover direct classification of spectra (Moshou et al, 2004), combined analysis of multiple vegetation indices (Behmann et al, 2014) and derivation of new, disease specific indices (Mahlein et al, 2013). Supervised approaches like neural networks (Wu et al, 2008), Support Vector Machines (Rumpf et al, 2010) and LDA (Suzuki et al, 2008) and unsupervised approaches like Self-Organizing Maps (SOM; Moshou et al, 2002) are used. Since label information for disease symptoms are hard to obtain and oftentimes erroneous, one-class classifiers (e.g. , Scholkopf et al, 2001; Tax and Duin, 2004) and unsupervised approaches are promising (e.g. , Wahabzada et al, 2015)

Methods

Results

Conclusion