EDGE COMPUTING ENABLED WIRELESS SENSOR NETWORKS: A CASE STUDY ON HYPERSPECTRAL IMAGE CLASSIFICATION
Keywords:
hyperspectral image classification, edge computing, wireless sensor network, dimensionality reduction, machine learningAbstract
This paper evaluates the use of edge computing in wireless sensor networks (WSNs) for hyperspectral image classification in space. Hyperspectral images are computationally demanding and require significant resources. Edge computing can reduce the amount of data transmitted from sensor nodes, thus reducing the overall bandwidth requirements and improving system efficiency. Processing data locally can also reduce latency, which is crucial in extreme environments where communication can be challenging. The SVM, Logistic Regression, and Random Forest algorithms are applied to Indian Pines and Salinas datasets, resulting in six classification scenarios. Software utilizing artificial intelligence algorithms is designed and tested using Google Collaboratory cloud-based platform. Additional dimensionality reduction technique is incorporated and evaluated to enhance classification accuracy. Results indicate that edge computing can improve the efficiency of hyperspectral image classification in space. This research provides valuable insights into the use of edge computing for hyperspectral image classification and has important implications for remote sensing applications in space.
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