Hyperspectral image (HSI) classification aims to assign each hyperspectral pixel with a proper land-cover label. Recently, convolutional neural networks (CNNs) have shown superior performance. To ...identify the land-cover label, CNN-based methods exploit the adjacent pixels as an input HSI cube, which simultaneously contains spectral signatures and spatial information. However, at the edge of each land-cover area, an HSI cube often contains several pixels whose land-cover labels are different from that of the center pixel. These pixels, named interfering pixels, will weaken the discrimination of spectral-spatial features and reduce classification accuracy. In this article, a spectral-spatial attention network (SSAN) is proposed to capture discriminative spectral-spatial features from attention areas of HSI cubes. First, a simple spectral-spatial network (SSN) is built to extract spectral-spatial features from HSI cubes. The SSN is composed of a spectral module and a spatial module. Each module consists of only a few 3-D convolution and activation operations, which make the proposed method easy to converge with a small number of training samples. Second, an attention module is introduced to suppress the effects of interfering pixels. The attention module is embedded into the SSN to obtain the SSAN. The experiments on several public HSI databases demonstrate that the proposed SSAN outperforms several state-of-the-art methods.
In this paper, we designed an end-to-end spectral-spatial residual network (SSRN) that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification. In this ...network, the spectral and spatial residual blocks consecutively learn discriminative features from abundant spectral signatures and spatial contexts in hyperspectral imagery (HSI). The proposed SSRN is a supervised deep learning framework that alleviates the declining-accuracy phenomenon of other deep learning models. Specifically, the residual blocks connect every other 3-D convolutional layer through identity mapping, which facilitates the backpropagation of gradients. Furthermore, we impose batch normalization on every convolutional layer to regularize the learning process and improve the classification performance of trained models. Quantitative and qualitative results demonstrate that the SSRN achieved the state-of-the-art HSI classification accuracy in agricultural, rural-urban, and urban data sets: Indian Pines, Kennedy Space Center, and University of Pavia.
Hyperspectral image classification (HSIC) methods based on convolutional neural network (CNN) continue to progress in recent years. However, high complexity, information redundancy, and inefficient ...description still are the main barriers to the current HSIC networks. To address the mentioned problems, we present a spatial-spectral dense CNN framework with a feedback attention mechanism called FADCNN for HSIC in this article. The proposed architecture assembles the spectral-spatial feature in a compact connection style to extract sufficient information independently with two separate dense CNN networks. Specifically, the feedback attention modules are developed for the first time to enhance the attention map with the semantic knowledge from the high-level layer of the dense model, and we strengthen the spatial attention module by considering multiscale spatial information. To further improve the computation efficiency and the discrimination of the feature representation, the band attention module is designed to emphasize the weight of the bands that participated in the classification training. Besides, the spatial-spectral features are integrated and mined intensely for better refinement in the feature mining network. The extensive experimental results on real hyperspectral images (HSI) demonstrate that the proposed FADCNN architecture has significant advantages compared with other state-of-the-art methods.
Hyperspectral image (HSI) classification is an important topic in the community of remote sensing, which has a wide range of applications in geoscience. Recently, deep learning-based methods have ...been widely used in HSI classification. However, due to the scarcity of labeled samples in HSI, the potential of deep learning-based methods has not been fully exploited. To solve this problem, a self-supervised learning (SSL) method with adaptive distillation is proposed to train the deep neural network with extensive unlabeled samples. The proposed method consists of two modules: adaptive knowledge distillation with spatial-spectral similarity and 3-D transformation on HSI cubes. The SSL with adaptive knowledge distillation uses the self-supervised information to train the network by knowledge distillation, where self-supervised knowledge is the adaptive soft label generated by spatial-spectral similarity measurement. The SSL with adaptive knowledge distillation mainly includes the following three steps. First, the similarity between unlabeled samples and object classes in HSI is generated based on the spatial-spectral joint distance (SSJD) between unlabeled samples and labeled samples. Second, the adaptive soft label of each unlabeled sample is generated to measure the probability that the unlabeled sample belongs to each object class. Third, a progressive convolutional network (PCN) is trained by minimizing the cross-entropy between the adaptive soft labels and the probabilities generated by the forward propagation of the PCN. The SSL with 3-D transformation rotates the HSI cube in both the spectral domain and the spatial domain to fully exploit the labeled samples. Experiments on three public HSI data sets have demonstrated that the proposed method can achieve better performance than existing state-of-the-art methods.
Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In this paper, we first ...point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them. If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point. This shows that how to select positive and negative training samples is important for current object detectors. Then, we propose an Adaptive Training Sample Selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of object. It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them. Finally, we discuss the necessity of tiling multiple anchors per location on the image to detect objects. Extensive experiments conducted on MS COCO support our aforementioned analysis and conclusions. With the newly introduced ATSS, we improve state-of-the-art detectors by a large margin to 50.7% AP without introducing any overhead. The code is available at https://github.com/sfzhang15/ATSS.
The first paper is titled “Glioma extraction from MR images employing Gradient Based Kernel Selection Graph Cut technique” by Jyotsna Dogra, Shruti Jain and Meenakshi Sood from Jaypee University of ...Information Technology, India. The seventh paper is “Noise-tolerant texture feature extraction through directional thresholded local binary pattern” by Sayed Mohamad Tabatabaei and Abdolah Chalechale from Razi University, Iran. The fourteenth paper is “A comprehensive survey on automatic facial action unit analysis” by Ruicong Zhi and Dezheng Zhang from University of Science and Technology Beijing, China and Mengyi Liu from Beijing Information Science and Technology University, China.
Convolutional neural networks (CNNs) have recently been demonstrated to be a powerful tool for hyperspectral image (HSI) classification, since they adopt deep convolutional layers whose kernels can ...effectively extract high-level spatial-spectral features. However, sampling locations of traditional convolutional kernels are fixed and cannot be changed according to complex spatial structures in HSIs. In addition, the typical pooling layers (e.g., average or maximum operations) in CNNs are also fixed and cannot be learned for feature downsampling in an adaptive manner. In this letter, a novel deformable CNN-based HSI classification method is proposed, which is called deformable HSI classification networks (DHCNet). The proposed network, DHCNet, introduces the deformable convolutional sampling locations, whose size and shape can be adaptively adjusted according to HSIs' complex spatial contexts. Specifically, to create the deformable sampling locations, 2-D offsets are first calculated for each pixel of input images. The sampling locations of each pixel with calculated offsets can cover the locations of other neighboring pixels with similar characteristics. With the deformable sampling locations, deformable feature images are then created by compressing neighboring similar structural information of each pixel into fixed grids. Therefore, applying the regular convolutions on the deformable feature images can reflect complex structures more effectively. Moreover, instead of adopting the pooling layers, the strided convolution is further introduced on the feature images, which can be learned for feature downsampling according to spatial contexts. Experimental results on two real HSI data sets demonstrate that DHCNet can obtain better classification performance than can several well-known classification methods.
Learning with limited data is a key challenge for visual recognition. Many few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the ...function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them leads to the target task. In this paper, we propose a novel approach to adapt the instance embeddings to the target classification task with a set-to-set function, yielding embeddings that are task-specific and are discriminative. We empirically investigated various instantiations of such set-to-set functions and observed the Transformer is most effective --- as it naturally satisfies key properties of our desired model. We denote this model as FEAT (few-shot embedding adaptation w/ Transformer) and validate it on both the standard few-shot classification benchmark and four extended few-shot learning settings with essential use cases, i.e., cross-domain, transductive, generalized few-shot learning, and low-shot learning. It archived consistent improvements over baseline models as well as previous methods, and established the new state-of-the-art results on two benchmarks.
Wind speed forecasting is important for high-efficiency utilisation of wind energy and management of grid-connected power systems. Due to the noise, instability and irregularity of atmosphere system, ...the current models based on raw historical data have encountered many problems. In this study, a deep novel feature extraction approach is developed based on stacked denoising autoencoders and batch normalisation. Then the deep features extracted from raw historical data are fed to long short-term memory (LSTM) neural networks for prediction. Meanwhile, density-based spatial clustering of applications with noise is employed to process the numerical weather prediction data. By picking out the abnormal samples, the representative training samples are selected to improve the efficiency of the model. For illustration and verification purposes, the proposed model is used to predict the wind speed of Wind Atlas for South Africa (WASA). Empirical results show that deep feature extraction can improve the forecasting accuracy of LSTM 49% than feature selection, indicating that proper feature extraction is crucial to wind speed forecasting. And the proposed model outperforms other benchmark methods at least 17%. Hence, the proposed model is promising for wind speed forecasting.
This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature ...extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a single framework, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise. Considering the multiscale characteristics inherent in vibration signals of a gearbox, a new multiscale convolutional neural network (MSCNN) architecture is proposed to perform multiscale feature extraction and classification simultaneously. The proposed MSCNN incorporates multiscale learning into the traditional CNN architecture, which has two merits: 1) high-level fault features can be effectively learned by the hierarchical learning structure with multiple pairs of convolutional and pooling layers; and 2) multiscale learning scheme can capture complementary and rich diagnosis information at different scales. This greatly improves the feature learning ability and enables better diagnosis performance. The proposed MSCNN approach is evaluated through experiments on a WT gearbox test rig. Experimental results and comprehensive comparison analysis with respect to the traditional CNN and traditional multiscale feature extractors have demonstrated the superiority of the proposed method.
PDF
