Extraction of spatial-spectral homogeneous patches and fractional abundances for field-scale agriculture monitoring using airborne hyperspectral images

Farm scale information is crucial for large scale agricultural monitoring and policymaking. Therefore, per-field agricultural land-use statistics is more informative than per-pixel statistics. However, per-field segmentation of hyperspectral image (HSI) is challenging due to the variability in spectral responses of different spectral bands within the neighbourhood. Moreover, manual digitization and modification of the field map or field boundaries at each airborne campaign (at different spatial resolution) or each growing season is a challenging task. To overcome this challenge, we developed an unsupervised method for segmenting the spatial-spectral homogeneous pixels and its fractional abundances in airborne HSIs. In the proposed approach, morphological operations based local spectral similarity measure combined with edge detector was applied to produce edge intensity map. Watershed segmentation was applied on the binary edge map to generate the spatially homogeneous land segments in the HSI. Then, online-dictionary-learning and sparse-unmixing have been performed on the HSI to compute the fractional abundance map of vegetation, soil and crop residue and to mask the other targets. The proposed method was tested on simulated HSI, benchmark HSIs as well as the image obtained from AirborneVisible-and-Infra-Red-Imaging-Spectrometer Next-Generation (AVIRIS-NG) sensor. Figure-of-merit, false-alarmcount and miss-count were applied to evaluate the performance of boundary detection methods. The results indicate that the fractional-spectral-similarity outperformed other distance measures in detecting spectrally homogeneous regions. It was also observed that integration of fractional-similarity with Sobel-edge-filter improved the performance of the edge detector. Edge-intensity-map generated using the fractional-similarity approach was employed as seed points for the watershed segmentation, which showed significant improvement in terms of accuracy as compared to other segmentation methods. We also demonstrated the robustness of our method on multiple HSI datasets spanning different regions. Moreover, the integration of spectral un-mixing with segmentation technique enables the identification of bare fields, harvested fields with crop-residues, and the fields with high vegetation canopy cover. The proposed method is automated with few parameters and it is operationally practical for large HSIs.