Semantic segmentation of crop areas in remote sensing imagery using spectral indices and multiple channels

Abstract

This study focuses on pixel-wise semantic segmentation of crop production regions by using satellite remote sensing multispectral imagery. One of the principal aims of the study is to find out whether the raw multiple channel inputs are more effective in the training process of the semantic segmentation models or if the formularized counterparts as the spectral indices are more effective. For this purpose, the vegetation indices NDVI, ARVI and SAVI and the water indices NDWI, NDMI, and WRI are employed as inputs. Additionally, using 8, 10 and 16 channels, multiple channel inputs are utilized. Moreover, all spectral indices are taken as separate channels to form a multiple channel input. We conduct deep learning experiments using two semantic segmentation architectures, namely U-Net and DeepLabV3+. Our results show that, in general, feeding raw multiple channel inputs to semantic segmentation models performs much better than feeding the spectral indices. Hence, regarding crop production region segmentation, deep learning models are capable of encoding multispectral information. When the spectral indices are compared among themselves, ARVI, which reduces the atmospheric scattering effects, achieves better accuracy for both architectures. The results also reveal that spatial resolution of multispectral data has a significant effect on the semantic segmentation performance, and therefore the RGB band, which has the lowest ground sample distance (0.31 m) outperforms multispectral bands and shortwave infrared bands.