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To effectively improve the performance of representation based classifier, a spatial spectral joint classification post-processing approach is proposed, based on the application of edge preserving BF (Bilateral Filtering) method. The proposed framework includes two key processes: (1) the classifier (such as SRC, CRC, or KSRC) based on sparse representation of each pixel is used to obtain softclassified probabilities belonging to each information class for each pixel; (2) spatial spectral joint BF for the soft-classified probabilities map. It is aimed to integrate context-aware information for each pixel class labels. Under the spatial guidance image, extracted from the three principle component, a BF is employed to get the refined probability maps. The BF considers not only the spatial distance but it also considers the image context-aware distance which significantly improves the classification results. Finally, the class label is obtained by choosing the maximum probability criteria. The experimental results on three benchmark hyperspectral data sets showed that the "local smoothing" is efficient and has a potential to achieve high classification accuracy. All the algorithms are implemented with equal number of labeled samples and comparative results are presented in terms of visual classification map and numerical classification results. The major advantages of proposed method are: it is simple, noniterative and easy to implement. Hence, the advantages lead to significant usage in real applications.

According to World Health Organization (WHO) report, every 40 seconds a person attempts suicide globally. Depression, one of the world’s most prevailing diseases has become a reason behind these suicides. It is believed that early diagnosis of major depressive disorder (MDD) can reduce the adversity of this heinous deformity. For few years various machine learning and advanced neurocomputing techniques are being utilized in Electroencephalogram (EEG) based detection of multiple neurological diseases. In the proposed study, an EEG based screening of MDD is presented while using various Machine Learning and one Deep Learning approach. The majority of previous EEG based MDD decoding research has concentrated on a limited features. It was necessary to conduct in-depth comparisons of different approaches, besides more detailed feature-based EEG analysis. This research starts with the creation of a complete feature-based framework, which is then further compared against the state of the art end to end techniques. The K-nearest neighbors (KNN) model outperformed the other models and gained an accuracy of 87.5%. While long short term memory (LSTM) model acquired an accuracy of 83.3%. This study can further support in clinical diagnosis of multiple stages of MDD and can attempt to provide an early intervention.