Abstract-This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minority class examples according to their level of difficulty in learning, where more synthetic data is generated for minority class examples that are harder to learn compared to those minority examples that are easier to learn. As a result, the ADASYN approach improves learning with respect to the data distributions in two ways: (1) reducing the bias introduced by the class imbalance, and (2) adaptively shifting the classification decision boundary toward the difficult examples. Simulation analyses on several machine learning data sets show the effectiveness of this method across five evaluation metrics.

Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework which divides the corresponding works into spectralfeature networks, spatial-feature networks, and spectral-spatialfeature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.

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Abstract:We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique. 1178-1181 (1991). 2. S. Bhat, I. V. Larina, K. V. Larin, M. E. Dickinson, and M. Liebling, "4D reconstruction of the beating embryonic heart from two orthogonal sets of parallel optical coherence tomography slice-sequences," IEEE Trans. Med. Imaging 32(3), 578-588 (2013). 3. P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, "Evaluation of optical coherence tomography retinal thickness parameters for use in clinical trials for neovascular age-related macular degeneration," Invest. Ophthalmol. Vis. Sci. 50(7), 3378-3385 (2009). 4. P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, "Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative agerelated macular degeneration," Retina 31(3), 453-463 (2011). 5. P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, "Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images," Biomed. Opt. Express 5(10), 3568-3577 (2014). 6. J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W.Gardner, "The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry DR and PRP effects on photoreceptor cell function," Invest. Ophthalmol. Vis. Sci. 57(1), 208-217 (2016). 7. C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G.Fujimoto, "Imaging of macular diseases with optical coherence tomography," Ophthalmology 102(2), 217-229 (1995