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- W4317464247 abstract "With their combined spectral depth and geometric resolution, hyperspectral remote sensing images embed a wealth of complex, non-linear information that challenges traditional computer vision techniques. Yet, deep learning methods known for their representation learning capabilities prove more suitable for handling such complexities. Unlike applications that focus on single-label, pixel-level classification methods for hyperspectral remote sensing images, we propose a multi-label, patch-level classification method based on a two-component deep-learning network. We use patches of reduced spatial dimension and a complete spectral depth extracted from the remote sensing images. Additionally, we investigate three training schemes for our network: Iterative, Joint, and Cascade. Experiments suggest that the Joint scheme is the best-performing scheme; however, its application requires an expensive search for the best weight combination of the loss constituents. The Iterative scheme enables the sharing of features between the two parts of the network at the early stages of training. It performs better on complex data with multi-labels. Further experiments showed that methods designed with different architectures performed well when trained on patches extracted and labeled according to our sampling method." @default.
- W4317464247 created "2023-01-20" @default.
- W4317464247 creator A5012339507 @default.
- W4317464247 creator A5055529222 @default.
- W4317464247 date "2023-01-17" @default.
- W4317464247 modified "2023-10-16" @default.
- W4317464247 title "Training Methods of Multi-label Prediction Classifiers for Hyperspectral Remote Sensing Images" @default.
- W4317464247 doi "https://doi.org/10.48550/arxiv.2301.06874" @default.
- W4317464247 hasPublicationYear "2023" @default.
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