Sharpend Cosine Similarity based Neural Network for Hyperspectral Image
  Classification
Muhammad Ahmad
Hyperspectral Image Classification (HSIC) is a difficult task due to high
inter and intra-class similarity and variability, nested regions, and
overlapping. 2D Convolutional Neural Networks (CNN) emerged as a viable network
whereas, 3D CNNs are a better alternative due to accurate classification.
However, 3D CNNs are highly computationally complex due to their volume and
spectral dimensions. Moreover, down-sampling and hierarchical filtering (high
frequency) i.e., texture features need to be smoothed during the forward pass
which is crucial for accurate HSIC. Furthermore, CNN requires tons of tuning
parameters which increases the training time. Therefore, to overcome the
aforesaid issues, Sharpened Cosine Similarity (SCS) concept as an alternative
to convolutions in a Neural Network for HSIC is introduced. SCS is
exceptionally parameter efficient due to skipping the non-linear activation
layers, normalization, and dropout after the SCS layer. Use of MaxAbsPool
instead of MaxPool which selects the element with the highest magnitude of
activity, even if it's negative. Experimental results on publicly available HSI
datasets proved the performance of SCS as compared to the convolutions in
Neural Networks.