A MULTIMODAL FUSION ARCHITECTURE COMBINING DTICF-3D- CNN–BI-LSTM AND RGB-ENHANCED 2D-CNN FOR EARTH SURFACE HSI CLASSIFICATION - M Preethi; T Idhaya; S kanagasankari; J Rexy (The Bioscan, 2026)
Hyperspectral imaging technology is one of the most efficient and growing technologies derived from Image Sensing technology. HSI classification contains rich spectral information which is used to identify and classify the information of the earth surface. Due to large dimensionality of spectral information, HSI classification still faces several challenges and also creates the problem of detecting the target of the classes accurately. To overcome this problem, we proposed a Spectral-Spatial Classification of HSI using fusion of 3-Dimensional Convolutional Neural Network with Bi Directional Long Short Term Memory Network (3D-CNN-BiLSTM)and 2-Dimensional-CNN (2D-CNN) approach. Finally, Markov Random Field (MRF) is utilized to encourage the neighboring pixel which helps to enhance the classification results. To prove the better performance of the proposed techniques, some of the existing Machine learning and deep learning techniques are compared. From this analysis, it is evaluated that, the proposed techniques provide the best results, when compared to other techniques.