Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density

Remote Sensing

卷:11

Etienne Belin¹  Pejman Rasti¹  Ali Ahmad¹  Salma Samiei¹  David Rousseau¹ 

[1] LARIS, UMR INRA IRHS, Université d’Angers, 62 avenue Notre Dame du Lac, 49000 Angers, France;

关键词: weed detection;    scatter transform;    deep learning;    machine-learning classification;    annotation;    synthetic data;    local binary pattern;   

DOI  :  10.3390/rs11030249

来源: DOAJ

【 摘 要 】

In this article, we assess the interest of the recently introduced multiscale scattering transform for texture classification applied for the first time in plant science. Scattering transform is shown to outperform monoscale approaches (gray-level co-occurrence matrix, local binary patterns) but also multiscale approaches (wavelet decomposition) which do not include combinatory steps. The regime in which scatter transform also outperforms a standard CNN architecture in terms of data-set size is evaluated ( ${10}^4$ instances). An approach on how to optimally design the scatter transform based on energy contrast is provided. This is illustrated on the hard and open problem of weed detection in culture crops of high density from the top view in intensity images. An annotated synthetic data-set available under the form of a data challenge and a simulator are proposed for reproducible science. Scatter transform only trained on synthetic data shows an accuracy of $85\%$ when tested on real data.

【 授权许可】

Unknown