Maitreya Shelare

Maitreya Shelare, Atharva Satam, Poonam Sonar et al.

This work presents a data-efficient variant of the Attention-Based Dual-Branch Complex Feature Fusion Network (CFFN) for hyperspectral image classification. The proposed model, termed DE-CFFN, retains the original two-stream structure: the Real-Valued Neural Network (RVNN) processes standard hyperspectral patches, while the Complex-Valued Neural Network (CVNN) handles their Fourier-transformed counterparts. The main contribution of this work lies in the feature extraction process and architectural enhancement. Factor Analysis is used for dimensionality reduction, offering improved latent feature representation over Principal Component Analysis. Additionally, both the RVNN and CVNN streams are structurally modified by successively halving the number of filters in the 3D convolutional layers to reduce complexity. The outputs of both branches are concatenated and passed through a Squeeze and Excitation (SE) block to enhance joint feature representation. Evaluated on the Pavia University and Salinas datasets, DE-CFFN achieves classification performance comparable to CFFN, while significantly reducing model size, memory consumption, and inference latency, making it suitable for real-time hyperspectral imaging applications.

Maitreya Shelare, Neha Shigvan, Atharva Satam et al.

The field of remote-sensing image classification has seen immense progress with the rise of convolutional neural networks, and more recently, through vision transformers. These models, with their self-attention mechanism, can effectively capture global relationships and long-range dependencies between the image patches, in contrast with traditional convolutional models. This paper introduces StrideNET, a dual-branch transformer-based model developed for terrain recognition and surface roughness extraction. The terrain recognition branch employs the Swin Transformer to classify varied terrains by leveraging its capability to capture both local and global features. Complementing this, the roughness extraction branch utilizes a statistical texture-feature analysis technique to dynamically extract important land surface properties such as roughness and slipperiness. The model was trained on a custom dataset consisting of four terrain classes - grassy, marshy, sandy, and rocky, and it outperforms benchmark CNN and transformer based models, by achieving an average test accuracy of over 99 % across all classes. The applications of this work extend to different domains such as environmental monitoring, land use and cover classification, disaster response and precision agriculture.