Land cover classification from hyperspectral images using regularized hybrid cnn and adam. Achieve high-accuracy land cover classification from hyperspectral images using a Regularized Hybrid CNN optimized with ADAM. This simplified model reaches 99.25% accuracy with reduced complexity.
The utilization of hyperspectral imagery offers enhanced detail and accuracy for environmental monitoring and natural resource management, particularly through land cover classification. Hyperspectral data capture spectral signatures across numerous wavelengths, allowing precise differentiation of various surface materials and land types. While numerous approaches have been proposed for hyperspectral image classification, many suffer from overly complex model structures and suboptimal performance, limiting their practical application. This study introduces a simplified yet effective architecture by implementing a Regularized Hybrid Convolutional Neural Network (CNN) optimized using Adaptive Moment Estimation (ADAM). The proposed model is evaluated on the widely used Pavia Center hyperspectral dataset to assess its performance in land cover classification tasks. The model achieves a notable Overall Accuracy of 99.25% and Average Accuracy of 97.50%, demonstrating its capability in handling high-dimensional hyperspectral data with reduced model complexity. Additionally, a comparative analysis with conventional CNN architectures is conducted, highlighting the superior performance and efficiency of the proposed approach. These findings underscore the potential of regularized hybrid CNNs as a reliable and scalable solution for hyperspectral image classification, especially in applications requiring high precision and reduced computational overhead.
This study presents a timely and relevant contribution to the field of hyperspectral image classification for land cover mapping. Addressing the pervasive issue of overly complex models and suboptimal performance in existing approaches, the authors propose a Regularized Hybrid Convolutional Neural Network (CNN) optimized using Adaptive Moment Estimation (ADAM). The core motivation to develop a simplified yet effective architecture for handling high-dimensional hyperspectral data, while simultaneously aiming for high precision and reduced computational overhead, is well-justified and aligns with practical demands in environmental monitoring and natural resource management. The methodology focuses on leveraging the strengths of a hybrid CNN architecture, enhanced with regularization, and fine-tuned using the ADAM optimizer. The model's performance is rigorously evaluated on the widely recognized Pavia Center hyperspectral dataset. The reported results are notably impressive, achieving an Overall Accuracy of 99.25% and an Average Accuracy of 97.50%. These figures, coupled with the claim of reduced model complexity, represent a significant advancement, indicating the model's capability in accurately differentiating various surface materials and land types. Furthermore, the abstract highlights a comparative analysis demonstrating the superior performance and efficiency of the proposed method over conventional CNN architectures, reinforcing its potential as a scalable solution. While the reported accuracies are exceptionally high and the focus on reduced complexity is commendable, the abstract leaves some room for further inquiry typical of a comprehensive journal review. The validation on a single dataset, albeit a widely used one, could benefit from expansion to other diverse hyperspectral datasets to robustly confirm generalizability. Additionally, while the comparison with "conventional CNN architectures" is noted, a more detailed benchmark against contemporary state-of-the-art hybrid or specialized hyperspectral classification models would provide a clearer context of its competitive standing. Nevertheless, the study successfully showcases the promising capabilities of regularized hybrid CNNs, offering a reliable and efficient framework that holds considerable potential for practical applications requiring high-precision land cover classification.
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