CNN-based salient features in HSI image semantic target prediction

Deep networks have escalated the computational performance in the sensor-based high dimensional imaging such as hyperspectral images (HSI), due to their informative feature extraction competency. Therefore in this work, we have extracted the informative features from different CNN models for the benchmark HSI datasets. The deep features have concatenated with spectral features to increase the informative knowledge in the image datacube. The feature concatenation has massively increased the size of datacube. Therefore, we have applied an unsupervised maximum object identification-based salient feature selection to identify the most informative features of datacube and discard the less informative features to reduce the computational time without compromising the accuracy. It is an unsupervised feature selection approach that transforms the data into scale space and achieved robust and strong features. In the previous CNN-based methods, raw features have directly fed to the MLP (multilayer perception) layers for target prediction whereas we have provided our salient features into a multi-core SVM-based set-up and have achieved high accuracy with low computational time as compared to the previous state-of-art techniques.