Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network

Hyperspectral remote sensing image (HRSI) can provide additional spectral information of objects and have been widely used in many fields. However, due to the complex environment of the HRSI gathering area, collecting the labeled samples of HRSI is time-consuming and labor-intensive. The scarcity of labeled samples is one of the major difficulties for HRSI analysis and processing. In this letter, a spatial–spectral prototypical network (SSPN) for HRSI is proposed for solving the problem of lack of labeled samples. The contribution of this letter is threefold. First, we design a novel local pattern coding algorithm to combine the spatial and spectral information of HRSI pixels based on spatial neighborhood correlation. Then, a spatial–spectral feature extraction algorithm based on 1-D convolutional neural network (1-D-CNN) is suggested to learn the spatial–spectral metric space where HRSI pixels can be correctly classified with only a few labeled samples. Finally, a novel prototype representation for HRSI in spatial–spectral metric space is proposed to better classify the mixed pixels existing in HRSI. The experimental results on three popular HRSI data sets demonstrate that the proposed SSPN is significantly better than the traditional algorithms.

The classification of hyperspectral remote sensing images (HSI) is an important task in the processing and application of hyperspectral images. Convolutional neural network (CNN) has significant advantages in the extraction and fusion of hyperspectral image spectrum and spatial features, so it has become a common method in the field of HSI classification. However, the spectral domain feature redundancy of hyperspectral images is very high, and the spatial domain feature structure is complex, which makes CNN more time-consuming and higher memory requirements in the feature extraction process. Based on this problem, lightweight networks with fewer parameters have gradually become the main application method in the field of HSI classification. In order to reduce the computational complexity of deep feature extraction and take into account the shallow features, a high-order nonlinear Ghost module is proposed on the basis of the original Ghost linear transformation module. Furthermore, in view of the independent characteristics of each dimension of HSI, a Trip-GhostNet is proposed, which simultaneously extracts and fuses features from three dimensions in a lightweight manner. According to the distribution characteristics of HSI, the influence of attention embedding methods in the high-order Ghost module of each branch on the feature extraction is compared and analyzed. The results show that the proposed model can reduce model calculations and improve classification accuracy, and is suitable for HSI classification problems.

To effectively utilize the rich spectral information of hyperspectral remote sensing images (HRSIs), the fractional Fourier transform (FRFT) feature of HRSIs is proposed to reflect the time-domain and frequency-domain characteristics of a spectral pixel simultaneously, and an FRFT order selection criterion is also proposed based on maximizing separability. Firstly, FRFT is applied to the spectral pixels, and the amplitude spectrum is taken as the FRFT feature of HRSIs. The FRFT feature is mixed with the pixel spectral to form the presented spectral and fractional Fourier transform mixed feature (SF2MF), which contains time–frequency mixing information and spectral information of pixels. K-nearest neighbor, logistic regression, and random forest classifiers are used to verify the superiority of the proposed feature. A 1-dimensional convolutional neural network (1D-CNN) and a two-branch CNN network (Two-CNNSF2MF-Spa) are designed to extract the depth SF2MF feature and the SF2MF-spatial joint feature, respectively. Moreover, to compensate for the defect that CNN cannot effectively capture the long-range features of spectral pixels, a long short-term memory (LSTM) network is introduced to be combined with CNN to form a two-branch network C-CLSTMSF2MF for extracting deeper and more efficient fusion features. A 3D-CNNSF2MF model is designed, which firstly performs the principal component analysis on the spa-SF2MF cube containing spatial information and then feeds it into the 3-dimensional convolutional neural network 3D-CNNSF2MF to extract the SF2MF-spatial joint feature effectively. The experimental results of three real HRSIs show that the presented mixed feature SF2MF can effectively improve classification accuracy.

RETRACTED: Deep learning network (DL-Net) based classification and segmentation of multi-class retinal diseases using OCT scans

The human face has biometric properties that are important for providing age information because of the aging process of the face. Automatic Age estimation is a difficult problem because the relationship between facial images and age is not very linear. Deep residual network (Resnet) is a neural network convolution architecture that was easier to optimize and can gain accuracy results from a considerably increasing depth. In this paper, we propose a new approach age estimation on convolution neural network (CNN) using the deep residual network (Resnet) model. Through the literature, Resnet achieves superior results when compared with other state-of-the-art image classifications. We compare a new generation of deep residual network called ResNeXt with Resnet and a basic linier regression model architecture.We user UTKFace dataset to evaluate the performance of residual network for age estimation of the range 1-100 years old. The result shows that the ResNeXt-50 (32×4d) architecture achieves a better age estimation results than Resnet-50 and linier regression.

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.

The aim of this review is to highlight novel artificial intelligence-based methods for the detection of optic disc abnormalities, with particular focus on neurology and neuro-ophthalmology. Methods for detection of optic disc abnormalities on retinal fundus images have evolved considerably over the last few years, from classical ophthalmoscopy to artificial intelligence-based identification methods being applied to retinal imaging with the aim of predicting sight and life-threatening complications of underlying brain or optic nerve conditions. Artificial intelligence and in particular newly developed deep-learning systems are playing an increasingly important role for the detection and classification of acquired neuro-ophthalmic optic disc abnormalities on ocular fundus images. The implementation of automatic deep-learning methods for detection of abnormal optic discs, coupled with innovative hardware solutions for fundus imaging, could revolutionize the practice of neurologists and other non-ophthalmic healthcare providers.

ABSTRACTThis study introduces the attention mechanism in hyperspectral remote sensing image (HSI) classification which can strengthen the information provided by important features, and weaken the non-essential information. We introduced the Squeeze-and-Excitation (SE) block embedded in three-dimensional densely connected convolutional network (3D-DenseNet) to form 3D-SE-DenseNet for HSI classifications. This model can learn a powerful network with low training costs and fast convergence speed, and avoids overfitting on small sample datasets. Two different 3D-SE-DenseNet models of 3D-SE-DenseNet and 3D-SE-DenseNet-BC were set up. Results from experiments show that the 3D-SE-DenseNet performs well on the Indian Pines, Pavia University, Botswana, and Kennedy Space Centre datasets.

Traditional power quality disturbances(PQDs) recognition methods are based on selecting features manually and then use classifiers to classify. For huge amounts of data, this method has the problem of insufficient features or redundant features, which leads to low recognition accuracy. With the development of deep learning technology, this paper proposes a PQDs identification method based on a deep residual network. This method links the two sets of convolutional layers in the traditional convolutional network into a residual block through short-cut. Then, it learns layer by layer through residual blocks, acquiring the deep features of data. Thus it realizes the identity mapping of the redundant layer by short-line connections to solve the network degradation problem of deep networks, and reduces the difficulty of network training. Finally, the softmax layer is used for classification. In order to test the anti-noise ability of the network, the deep residual network model is trained with the data added with Gaussian white noise. Simulation results show that, comparing with ordinary convolutional networks, the method proposed can accurately identify 7 types of single disturbances and 5 types of composite disturbances in different noise conditions. Furthermore, it has higher robustness and better network convergence speed.

As a relatively weak part of cable insulation, T-type cable terminals will have insulation defects due to process, installation, and other problems, resulting in partial discharge. Therefore, this paper uses Deep Convolution Neural Network (DCNN) and Empirical Mode Decomposition (EMD) to identify the partial discharge type of a 10 kV T-type cable terminal. This method uses the partial discharge experimental platform of the T-type cable terminal to collect the partial discharge signal. After the original signal that is difficult to identify is decomposed by EMD, a series of intrinsic mode components (IMFs) that are easy to locate are obtained. The deep learning network model is used to identify the defect type of the IMF signal. The results show that the overall defect recognition rate of this method reaches 95.3%. Compared with the traditional random forest algorithm (RF), the 10 kV T-type terminal partial discharge type recognition method based on EMD–DCNN is considered in this paper. The recognition accuracy of the main insulation scratch, bushing fouling, and joint loosening defects is higher than that of the traditional mechanical learning algorithm, RF, indicating that the method adopted in this paper can more effectively and accurately identify the defect type.

MSST-ResNet: Deep multi-scale spatiotemporal features for robust visual object tracking

An end-to-end deep convolutional neural network (CNN) based on deep residual network (ResNet) was proposed to efficiently reconstruct reliable T2 mapping from single-shot overlapping-echo detachment (OLED) planar imaging. The training dataset was obtained from simulations that were carried out on SPROM (Simulation with PRoduct Operator Matrix) software developed by our group. The relationship between the original OLED image containing two echo signals and the corresponding T2 mapping was learned by ResNet training. After the ResNet was trained, it was applied to reconstruct the T2 mapping from simulation and in vivo human brain data. Although the ResNet was trained entirely on simulated data, the trained network was generalized well to real human brain data. The results from simulation and in vivo human brain experiments show that the proposed method significantly outperforms the echo-detachment-based method. Reliable T2 mapping with higher accuracy is achieved within 30 ms after the network has been trained, while the echo-detachment-based OLED reconstruction method took approximately 2 min. The proposed method will facilitate real-time dynamic and quantitative MR imaging via OLED sequence, and deep convolutional neural network has the potential to reconstruct maps from complex MRI sequences efficiently.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Currently, video scene segmentation is an important part of realizing content-based video retrieval (CBVR). Aiming at the problem that low efficiency of video scene segmentation in CBVR, this paper proposed a multi-modal video scene segmentation optimization algorithm based on feature extraction of convolutional neural network (CNN). According to the large amount of information contained in the multi-modal data of video, the VGG19 network has been improved in a targeted manner and the underlying features and semantic features of various modes are extracted from each video shots. By forming these features into vectors and using the method such as triplet loss learning and shot similarity calculation, scene segmentation task is converted to a binary classification problem for shot boundary. Then the scoring mechanism is established to optimize the results, finally the scene segmentation task is completed. Experimental results show that the algorithm can be effective in video scene segmentation, and the overall recall and precision can reach 85.77% and 87.01%, respectively. Compared with the shot similarity graph method, two indicators have increased by 10% and 9% respectively. Compared with the DeepSSS method that also uses the deep learning network model, the comprehensive metric F-messure has increased by 8%.

Seeing through disguise: Getting to know you with a deep convolutional neural network