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 optimizationbased 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 modelsto 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 thenonlinear 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 dataHiperspektralayrıştırma, görüntünün içindeki malzemeleri tanımlamayı ve bu malzemelerekarşılık gelen bolluk değerlerini tahmin etmeyi amaçlamaktadır. Bu tez, malzemelerin spektral imzalarının mevcut olduğu durum için optimizasyona dayalı bir bolluk tahmin yöntemi ve malzemelerin spektral imzalarının olmadığı durumlar için derin öğrenme tabanlı bir hiperspektral ayrıştırma yöntemi önermektedir.İlk çalışmada sunulan bolluk tespit algoritması gerçek verilerin tek bir modelle ifade edilemeyecek kadar karmaşık etkileşimler içerebilmesi varsayımına dayanmaktadır. Bu nedenle, gerçek verilerde bolluk tespiti yapılırken çoklu model kullanılması hedeflenmiştir. Önerilen optimizasyon tabanlı bolluk tespit algoritması, hedef pikseleyakın bir hata oranına ulaşılana kadar doğrusal karışım modelini varsayan bir yaklaşımı benimser. Optimizasyon algoritması daha sonra maliyet fonksiyonunu, olası karışım modelleri için yeniden tanımlayarak işleme devam eder. Kullanılan çeşitli optimizasyon algoritmaları ve uzaklık metrikleriarasında, sıralı ikinci dereceden programlama ve spektral açı haritalama kombinasyonu ile önerilen çözüm, diğer arama yöntemleri ve temel algoritmalardan daha başarılı bulunmuştur. Bu tezin ikinci katkısı olarak, hiperspektral görüntülerde komşuluk bilgisinin ayrıştırma problemi için yeterince kullanılmadığı gözlemlenerek hiperspektral ayrıştırmaiçin yeni bir 3 boyutlu evrişimli kodlayıcı tabanlı derin öğrenme yöntemi önerilmiştir. Doğrusal olmayan karıştırmanın daha önce sunulmuş derin öğrenme tabanlı hiperspektral ayrıştırma çalışmalarında yeterince ele alınmadığı göz önüne alındığında, önerilen yöntem doğrusal olmayan karışım modellerini 3D evrişimli kodlayıcı yapısıyla çözmek için tasarlanmıştır. Önerilen yöntem, sentetik ve gerçek veriler üzerinde iyi bilinen saf malzeme çıkarma ve bolluk tahmini algoritmalarından daha iyi performans göstermiştir.Ph.D. - Doctoral Progra

Uzaktan algılama için hiperspektral imge sınıflandırıcıları üzerine bir inceleme.

Hyperspectral image processing is improved by the capabilities of multispectral image processing with high spectral resolution. In this thesis, we explored hyperspectral classification with Support Vector Machines (SVM), Maximum Likelihood (ML) and KNearest Neighborhood algorithms. We analyzed the effect of training data on classification accuracy. For this purpose, we implemented three different training data selection methods; first N sample selection, randomly N sample selection and uniformly N sample selection methods. We employed Principal Component Analysis (PCA) as preprocessing method and conducted experiments with different number of principal components for all three classification algorithms. As a post-processing method following pixelwise classification, filtering with 3x3 window and majority voting with meanshift segmentation methods are used to incorporate spatial information over spectral information. The experiments showed that without using pre-processing and post-processing SVM procures better classification accuracies than the other algorithms for all training data sizes. ML is inferior for lower number of training data samples but improves its performance with lower number of principal components. K-NN algorithm provides almost the same accuracies for more than 10 principal components. PCA usage does not improve SVM performance but decreases classification time for larger scenes. Filtering with 3x3 window method improves the classification accuracy by 4-5%. However, spatial information usage by employing majority voting with meanshift segmentation method performs better than filtering 3x3 window. Classification with both pre-processing and post-processing improves classification accuracy and decreases classification time. The largest improvement is for the ML method with lower number of training data.M.S. - Master of Scienc

Improved Hyperspectral Vegetation Detection Using Neural Networks with Spectral Angle Mapper

Hyperspectral images have been used in many areas including city planning, mining and military decision support systems. Hyperspectral image analysis techniques have a great potential for vegetation detection and classification with their capability to identify the spectral differences across the electromagnetic spectrum due to their ability to provide information about the chemical compositions of materials. This study introduces a vegetation detection method employing Artificial Neural Network (ANN) over hyperspectral imaging. The algorithm employed backpropagation MLP algorithm for training neural networks. The performance of ANN is improved by the joint use with Spectral Angle Mapper (SAM). The algorithm first obtains the certainty measure from ANN, following the completion of this process, every pixels' angular distance is computed by SAM. The certainty measure is divided by angular distance. Results from ANN, SAM and Support Vector Machine (SVM) algorithms are compared and evaluated with the result of the algorithm. Limited number of training samples are used for training. The results demonstrate that joint use of ANN and SAM significantly improves classification accuracy for smaller training sample

Improvement of hyperspectral classification accuracy with limited training data using meanshift segmentation

In this study, the performance of hyperspectral classification algorithms with limited training data investigated. Support Vector Machines (SVM) with Gaussian kernel is used. Principle Component Analysis (PCA) is employed for preprocessing and meanshift segmentation is used to incorporate spatial information with spectral information to observe the effect spatial information. Pattern search algorithm is used to optimize meanshift segmentation parameters. The performance of the algorithm is demonstrated on high resolution Pavia University hyperspectral data

Improvements on hyperspectral classification algorithms

This study investigates the effect of training set selection strategy on classification accuracy of hyperspectral images. This effect is analyzed in conjunction with three other factors, namely the use principal component analysis on the input data, and the use of spatial information and choice of classifier. Support Vector Machines (SVM) and Maximum Likelihood (ML) classifiers are used for demonstration. Meanshift segmentation and majority voting are used for inclusion of spatial information. The effect of the training data size and sampling strategy is demonstrated over the high resolution Pavia University hyperspectral data

Improvement of Hyperspectral Classification Accuracy with Limited Training Data Using Meanshift Segmentation

In this study, the performance of hyperspectral classification algorithms with limited training data investigated. Support Vector Machines (SVM) with Gaussian kernel is used. Principle Component Analysis (PCA) is employed for preprocessing and meanshift segmentation is used to incorporate spatial information with spectral information to observe the effect spatial information. Pattern search algorithm is used to optimize meanshift segmentation parameters. The performance of the algorithm is demonstrated on high resolution Pavia University hyperspectral data

The Effect of Training Data on Hyperspectral Classification Algorithms

In this study, the performance of different hyperspectral classification algorithms with the same training set is investigated. In addition, the effect of the dimension and sampling strategy for the training set selection is demonstrated. Support Vector Machines (SVM), K-Nearest Neighbor (K-NN) and Maximum Likelihood (ML) methods are used. The contribution of using spatial information with spectral information is observed. Meanshift segmentation and window weighting methods are used for spatial information. High resolution Pavia University hyperspectral data and Indian Pines data are used in this study

SPEL: Development af a New Spectral Signature Library for Food Products

Hyperspectral Imaging (HSI) has become a widely used nondestructive inspection method in Food Quality Assessment (FQA). In FQA studies, HSI is used to measure light reectance characteristics (spectra) of food products at different wavelengths. Obtained spectral characteristics of food products reveal detailed information such as freshness, suitability for consumption, potential diseases etc. In this study, development phases and features of METU - Spectral Signature Library of Food Products (METU SPEL) database based on U.S. Department of Agriculture - USDA National Nutrient Database for Standard Reference, Release 26 is described. The main objective of METU SPEL is to build a library that contains spectral characteristics of food products to assist researchers working on FQA with HSI

Mobil Zararlı Yazılımların Analizi Sistemi Geliştirilmesi

“Mobil Zararlı Yazılımların Analizi Sistemi Geliştirilmesi” projesinin amacı, Android işletim sistemini kullanan akıllı mobil cihazlar üzerindeki yazılımların kurulma aşamasında ve kurulum sonrasında sürekli olarak belirli aralıklarla statik ve dinamik analizlerinin yapılarak zararlı yazılımların tespit edilmesi için bir analiz modelinin geliştirilmesi ve uygulanmasıdır. Proje kapsamında öncelikli olarak, mobil yazılımların statik olarak analizini sağlayacak bir altyapı kurulması planlanmaktadır. Geleneksel statik analiz modeli kurulduktan sonra dinamik analiz modeli oluşturularak zararlı yazılımların fiziksel mobil cihazlar üzerinde ve yazılım tabanlı emülasyon ortamlarında incelenmesine olanak sağlanması planlanmaktadır. Dinamik analiz modeli Android tabanlı işletim sistemlerinde geliştirilecek ve uygulanacaktır.Mobil zararlı yazılımların analizi için gerekli olan altyapının geliştirilmesi kapsamında tersine çevirme (disassembler) ve geri derleme araçlarının incelenmesi ve geliştirilmesi, Android işletim sisteminin kullandığı Dalvik sanal makinesinin incelenmesi, hata ayıklama araçlarının incelenmesi ve geliştirilmesi, statik ve dinamik analiz modellerinin haberleşmesinin sağlanması gibi ihtiyaç duyulacak fonksiyonların gerçekleştirilmesi planlanmaktadır