Sparse Coding Based Feature Representation Method for Remote Sensing Images

In this dissertation, we study sparse coding based feature representation method for the classification of multispectral and hyperspectral images (HSI). The existing feature representation systems based on the sparse signal model are computationally expensive, requiring to solve a convex optimization problem to learn a dictionary. A sparse coding feature representation framework for the classification of HSI is presented that alleviates the complexity of sparse coding through sub-band construction, dictionary learning, and encoding steps. In the framework, we construct the dictionary based upon the extracted sub-bands from the spectral representation of a pixel. In the encoding step, we utilize a soft threshold function to obtain sparse feature representations for HSI. Experimental results showed that a randomly selected dictionary could be as effective as a dictionary learned from optimization. The new representation usually has a very high dimensionality requiring a lot of computational resources. In addition, the spatial information of the HSI data has not been included in the representation. Thus, we modify the framework by incorporating the spatial information of the HSI pixels and reducing the dimension of the new sparse representations. The enhanced model, called sparse coding based dense feature representation (SC-DFR), is integrated with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) classifiers to discriminate different types of land cover. We evaluated the proposed algorithm on three well known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit (SOMP) and image fusion and recursive filtering (IFRF). The results from the experiments showed that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification. To further verify the power of the new feature representation method, we applied it to a pan-sharpened image to detect seafloor scars in shallow waters. Propeller scars are formed when boat propellers strike and break apart seagrass beds, resulting in habitat loss. We developed a robust identification system by incorporating morphological filters to detect and map the scars. Our results showed that the proposed method can be implemented on a regular basis to monitor changes in habitat characteristics of coastal waters

A Volume Sphering Analysis for MRI Brain Tissue Classification and Volume Calculation

This dissertation develops a volume sphering analysis (VSA) approach to tissue classification and volume calculation of multispectral magnetic resonance (MR) brain images. It processes all multispectral MR image slices as an image cube while using only one set of training samples obtained from a single multispectral image slice to perform image analysis. In order to make the selected one slice set of training samples also applicable to other MR image slices an extrapolation algorithm is particularly designed for this purpose. This significantly reduces tremendous burden on radiologists' selection of training samples as well as computational cost. In this work, we propose the following two different experiments by VSA approach. One is a supervised classification, Supervised Volume Sphering Analysis (SVSA) techniques were considered and analyzed by experiments for MR classification where the required complete knowledge of each MR tissue substance was obtained from the prior knowledge based on ground truth and their anatomical structures. The other is an unsupervised classification, Unsupervised Volume Sphering Analysis (UVSA) techniques could automatically classify healthy brain images with no inputs from operators and the results would be operator-independent. To further resolve instability and inconsistency issues resulting from a single slice set of training samples, an iterative Fisher's linear discriminant analysis (IFLDA) is also developed to be coupled with SVSA or UVSA to improve the traditional slice-by-slice MR image classification. Experimental results demonstrate that SVSA or UVSA using one set of training samples in conjunction with IFLDA not only performs comparably to approaches using training samples from individual image slices but also saves significant time of selecting training samples and computational cost.本研究提出一種體素球面化分析演算法於磁振造影像之腦組織分類與體積計算,它處理所有的高頻譜磁振造影像切片，視其為一個影像立方體,並在執行影像分析時，僅使用一組從一個單一高頻譜影像切片獲得的訓練樣本即可。為了使所選擇的一組切片訓練樣本也可以適用於其他的磁振造影像切片，為此目的特別設計一個外推法演算法，這有效地降低了放射科醫生挑選訓練樣本的沉重負擔，以及計算成本。在這項工作中，我們由體素球面化分析法提出以下兩種不同的實驗，一種是監督式體素球面化分析法：此技術被認為可以由實驗中，通過對磁振造分類的分析。但是，先要有腦部磁振造影像組織的參考標準及解剖結構為基礎之完整知識;另一種是非監督式體素球面化分析法：此技術可以自動地對正常腦部分類，而操作者無須任何輸入，結果亦獨立於操作者。更為了進一步解決從一組單一切片的訓練樣本，所造成不穩定和不一致問題，也提出體素球面化分析結合疊代式費雪線性辨別分析法，改進傳統磁振造影像切片逐一分類方式。實驗結果證明，此方法不僅執行同等使用從個別的影像切片挑選訓練樣本的方法，也節省了挑選訓練樣本的有效時間和計算的成本。Acknowledgements……………………………………………………………………ⅰ 摘要…………………………………………………………………………………….ⅱ Abstract………………………………………………………………………………..ⅲ Table of Contents……………………………………………………………………...ⅴ List of Figures…………………………………………………………………………ⅶ List of Tables…………………………………………………………………………..ⅸ Chapter 1. Introduction…………………………………………….….....1 1.1 Volume Sphering Analysis (VSA) in MR Image analysis…………………….......1 1.2 Unsupervised Volume Sphering Analysis (UVSA) in MR Image Analysis….…...5 1.3 Overview……………………………………………….…………………………8 Chapter 2. Background…………………….…………………….……….9 2.1 Magnetic Resonance Image (MRI) ………………………………………………9 2.1.1 Facilities of MRI…………………………………………………………….10 2.1.2 Slices and orientations………………………………………………………12 2.1.3 Pulse sequence and contrast mechanisms in MRI……………..…………....13 2.2 Independent component analysis (ICA) …………………………………...........15 2.2.1 Standard ICA……………………………………………………………….15 2.2.1.1 Conditions…...…...…...…...…...…...…...…...…...…...…...…...…...….17 2.2.1.2 Preprocessing for ICA…...…...…....…...…...…...…...……......…...…...17 2.2.1.2.1 Centering…...…...…....……...…...…......…...…...…......…...….......18 2.2.1.2.2 Whitening...…...…....……...…...…......…......…......…...…..............19 2.2.2 FastICA Algorithm………………………………………………………….20 2.2.3 ICA applied to the multispectral magnetic resonance (MR) brain images.....24 2.3 Theory of Support Vector Machine (SVM)…………………………………......25 2.4 Fisher's Linear Discriminant Analysis (FLDA)………………………………...29 2.5 Pixel Purity Index (PPI)…………………………………………………………31 Chapter 3. Mathods…………………………………………………………...36 3.1 Finding Training Samples from a Single MR Image Slice……………………...38 3.2 Extrapolation of Training Samples……………………………………………....44 3.3 IFLDA Classification………………………………………………………….....46 Chapter 4. Supervised Experiments…………………………………..49 4.1 Synthetic Brain Image Experiments…………………………………………......49 4.2 Real Image Experiments……………………………………………………........67 4.3 Conclusion……………………………………………………………….............71 Chapter 5. Unsupervised VSA classification…………………………...72 5.1 Unsupervised Training Sample Finding Algorithm (UTSFA)………..................72 5.2 UVSA-IFLDA Classification Algorithm………………………….......................76 5.3 Synthetic Brain Image Experiments …………………………………………….77 5.3.1 UVSA was developed to effectively segment multi-slice data of multispectral brain MRI……………………………………………………….…………..77 5.3.2 Quantitative Analysis………………………………………………….........82 5.4 Real Image Experiments…………………………………………………............84 5.5 Conclusion………………………………………………………………............88 Chapter 6. Conclusions…………………………………..……………....90 Reference…………………………………………………………….…...92 Appendix…………………………………………………………………9