SepHRNet: Generating High-Resolution Crop Maps from Remote Sensing
  imagery using HRNet with Separable Convolution
Priyanka Goyal Sohan Patnaik Adway Mitra Manjira Sinha
The accurate mapping of crop production is crucial for ensuring food
security, effective resource management, and sustainable agricultural
practices. One way to achieve this is by analyzing high-resolution satellite
imagery. Deep Learning has been successful in analyzing images, including
remote sensing imagery. However, capturing intricate crop patterns is
challenging due to their complexity and variability. In this paper, we propose
a novel Deep learning approach that integrates HRNet with Separable
Convolutional layers to capture spatial patterns and Self-attention to capture
temporal patterns of the data. The HRNet model acts as a backbone and extracts
high-resolution features from crop images. Spatially separable convolution in
the shallow layers of the HRNet model captures intricate crop patterns more
effectively while reducing the computational cost. The multi-head attention
mechanism captures long-term temporal dependencies from the encoded vector
representation of the images. Finally, a CNN decoder generates a crop map from
the aggregated representation. Adaboost is used on top of this to further
improve accuracy. The proposed algorithm achieves a high classification
accuracy of 97.5\% and IoU of 55.2\% in generating crop maps. We evaluate the
performance of our pipeline on the Zuericrop dataset and demonstrate that our
results outperform state-of-the-art models such as U-Net++, ResNet50, VGG19,
InceptionV3, DenseNet, and EfficientNet. This research showcases the potential
of Deep Learning for Earth Observation Systems.