Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .

Deep learning classifiers for hyperspectral imaging: A review

In this research, we proposed a novel 14-layered deep convolutional neural network (14-DCNN) to detect plant leaf diseases using leaf images. A new dataset was created using various open datasets. Data augmentation techniques were used to balance the individual class sizes of the dataset. Three image augmentation techniques were used: basic image manipulation (BIM), deep convolutional generative adversarial network (DCGAN) and neural style transfer (NST). The dataset consists of 147,500 images of 58 different healthy and diseased plant leaf classes and one no-leaf class. The proposed DCNN model was trained in the multi-graphics processing units (MGPUs) environment for 1000 epochs. The random search with the coarse-to-fine searching technique was used to select the most suitable hyperparameter values to improve the training performance of the proposed DCNN model. On the 8850 test images, the proposed DCNN model achieved 99.9655% overall classification accuracy, 99.7999% weighted average precision, 99.7966% weighted average recall, and 99.7968% weighted average F1 score. Additionally, the overall performance of the proposed DCNN model was better than the existing transfer learning approaches.

/pdf/plant-disease-detection-using-deep-convolutional-neural-3ou6cblv.pdf

Plant Disease Detection Using Deep Convolutional Neural Network

Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain

We present a method based on curvelet transform, LBP, ANOVA and PL classifier.We validate the proposed approach considering the metrics accuracy and AUC.The features was evaluated by applying the DT, RaF, SVM and PL classifiers.The proposed approach achieved AC values among 91% and 100%.The method was tested on the datasets: DDSM, BCDR-FMR, BCDR-DMR and UCSB-BB. In computer-aided diagnosis one of the crucial steps to classify suspicious lesions is the extraction of features. Texture analysis methods have been used in the analysis and interpretation of medical images. In this work we present a method based on the association among curvelet transform, local binary patterns, feature selection by statistical analysis and distinct classification methods, in order to support the development of computer aided diagnosis system. The similar features were removed by the statistical analysis of variance (ANOVA). The understanding of the features was evaluated by applying the decision tree, random forest, support vector machine and polynomial (PL) classifiers, considering the metrics accuracy (AC) and area under the ROC curve (AUC): the rates were calculated on images of breast tissues with different physical properties (commonly observed in clinical practice). The datasets were the Digital Database for Screening Mammography, Breast Cancer Digital Repository and UCSB biosegmentation benchmark. The investigated groups were normal-abnormal and benign-malignant. The association of curvelet transform, local binary pattern and ANOVA with the PL classifier achieved higher AUC and AC values for all cases: the obtained rates were among 91% and 100%. These results are relevant, specially when we consider the difficulties of clinical practice in distinguishing the studied groups. The proposed association is useful as an automated protocol for the diagnosis of breast tissues and may contribute to the diagnosis of breast tissues (mammographic and histopathological images).

LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues

http://digital.csic.es/bitstream/10261/132972/1/Real-time-Human-Robot.pdf

A real-time Human-Robot Interaction system based on gestures for assistive scenarios

Recent research in several fields such as Biotechnology and Healthcare has uncovered a vast number of applications where 3D Electron Microscopy (EM) is useful. However, images produced by 3D EM are in most cases severely degraded. These degradations arise due to a multitude of reasons, e.g. the complex electronics in the system, magnetic lens aberration, heating and motion stability, charging, etc. Although the raw, degraded images are currently used for analysis, their usefulness is limited because the degradations make visual distinction and automated analysis of biological features very difficult. In this work, we give an analysis of noise, as one of the most important degradations in 3D EM imaging. Next, we propose a Non-Local Means image restoration algorithm that exploits the derived noise characteristics. The proposed algorithm yields significant improvements compared to other state-of-the-art image restoration algorithms.

/pdf/advances-in-visual-computing-12th-international-symposium-1vwqpc8a1l.pdf

Advances in visual computing: 12th international symposium, ISVC 2016 Las Vegas, NV, USA, december 12–14, 2016 proceedings, part II

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Advances in visual computing: 12th international symposium, ISVC 2016 Las Vegas, NV, USA, december 12–14, 2016 proceedings, part II