Bio-Inspired Deep Learning for Enhanced Remote Sensing Image Interpretation

Abstract

This research explores the application of bio-inspired deep learning architectures to improve the accuracy and efficiency of remote sensing image interpretation. The project focuses on designing and implementing neural networks mimicking biological visual processing pathways, aiming to overcome limitations of traditional convolutional neural networks (CNNs) in handling complex, high-dimensional remote sensing data. Results demonstrate improved performance in object detection and classification tasks compared to existing CNN-based approaches, suggesting a promising avenue for enhancing remote sensing analysis. Further research will investigate the scalability and robustness of the proposed methods across diverse datasets and sensing modalities.

Introduction

Remote sensing plays a crucial role in various applications, including environmental monitoring, urban planning, and disaster management. Accurate and efficient interpretation of remote sensing images is essential, yet challenging due to factors like high dimensionality, spectral variability, and noise. Traditional image processing techniques often struggle with these complexities. Deep learning, particularly CNNs, has shown significant promise, but these models can suffer from limitations in computational efficiency and the ability to effectively capture subtle spatial relationships within the imagery. Bio-inspired neural networks, drawing inspiration from the human visual system, offer a potential solution by incorporating mechanisms for hierarchical feature extraction and attentional processing. This research aims to bridge this gap by leveraging the strengths of bio-inspired deep learning.

Technical Features

• Incorporates biologically-inspired models (e.g., Spiking Neural Networks, Vision Transformers)
• Handles hyperspectral and multispectral satellite image data
• Advanced attention mechanisms and adaptive feature extraction
• Improved accuracy in classification and segmentation tasks
• Tested on benchmark remote sensing datasets (e.g., UC Merced, EuroSAT)