Abstract:
Heavy metal contamination in soil is of great danger for the environment as well as
for the human health. This is owed to their hazardous toxicity alongside with their
ability to be easily captured by crops and plants. Detection of soil contamination is a
major step that aids in the soil rehabilitation process. Our goal in this project is to
explore the potential of using hyperspectral imaging and deep Learning techniques
to identify contamination in real time. The large size of the hyperspectral image
and the relatively limited resources in onboard processors make the real-time classi-
fication a challenging task. In this work, we propose a novel approach for real-time
hyperspectral image classification using optimal spatial-spectral input. The optimal
input consisted of the main pixel with two of its spatial neighbors. Two novel deep
learning models based on the optimized three pixel method were developed. The first
is a Deep Neural Network (DNN) model focused on fast online classification and the
second is a Recurrent Neural Network (RNN) model focused on offline classification
with enhanced accuracy. These models were evaluated using four datasets, three
agricultural datasets, and the Sydney Bridge dataset. The DNN model achieved a
maximum accuracy of 97% with an inference speed of 83,665 pixels per second while
the RNN model achieved a maximum accuracy of 99% with an inference speed of
25,000 pixel per second. When compared, the DNN model is more suited for quick
real-time applications while the RNN model is more suited for applications where
accuracy is critical. Furthermore, in terms of latency, our approach maximized the
preprocessing speed compared to other spatial-spectral methods. The attained fast
speed in preprocessing and inference is applicable for Real-time applications.
