Hiperspektral görüntülerde optimizasyon ve derin öğrenme tabanlı çok modelli bolluk tahmini ve ayrıştırma algoritmaları

Hyperspectral unmixing aims to identify the materials within the pixels of an image and estimate the corresponding abundance values of these materials. This thesis proposes an optimization based abundance estimation method for the case where the spectral signatures of the materials are available, and a deep learning based hyperspectral unmixing method for the case where the spectral signatures of the materials are unavailable. The proposed abundance estimation algorithm assumes that real data can contain complex interactions that cannot be modeled with a single model, and therefore, use multiple mixing models for determining the abundance of real data. The proposed optimization-based coarse-to-fine estimation algorithm first adopts a linear mixing model for the tested pixel until the error between the reconstructed and original pixel is smaller than a threshold. The algorithm then proceeds by integrating the other nonlinear mixing models to the cost function. Among various utilized optimization algorithms and metrics, the proposed solution with the sequential quadratic programming and spectral angle mapper combination is found more successful than other search methods and baseline algorithms. As the second contribution of this thesis, a new 3D convolutional encoder based deep learning method is proposed for hyperspectral unmixing by observing that the local neighborhood information is not sufficiently used for the unmixing problem in hyperspectral images. Given that nonlinear mixing has not been adequately covered in deep learning based hyperspectral unmixing literature, the proposed method is especially designed to solve the nonlinear mixture models with the 3D convolutional encoder structure. The proposed method gives better performance than the well-known pure material extraction and abundance detection algorithms on synthetic and real data.