Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV

The spectral-spatial classification of high spatial resolution RGB images obtained from unmanned aerial vehicles (UAVs) for detection of tomatoes in the image is presented. Bayesian information criterion (BIC) was used to determine the optimal number of clusters for the image. Spectral clustering was carried out using K-means, expectation maximisation (EM) and self-organising map (SOM) algorithms to categorise the pixels into two groups i.e. tomatoes and non-tomatoes. Due to resemblance in spectral intensities, some of the non-tomato pixels were grouped into the tomato group and in order to remove them, spatial segmentation was performed on the image. Spatial segmentation was carried out using morphological operations and by setting thresholds for geometrical properties. The number of pixels grouped in the tomato cluster is different for each clustering method. EM doesn't pick up the land patches as tomato pixels. As a result, the size of the tomatoes picked up is different than K-means and SOM. Since threshold values chosen for carrying out spatial segmentation are shape and size dependent, different threshold values are applied to different methods of clustering. A synthetic image of 12 × 12 pixels with different labels is created to illustrate the effect of each method used for spatial segmentation on the clustered image. Two representative UAV images captured at different heights from the ground were used to demonstrate the performance of the proposed method. Results and comparison of performance parameters of different spectral-spatial classification methods were presented. It is observed that EM performed better than K-means and SOM.