Jun Yue

Bio: Jun Yue is an academic researcher from Peking University. The author has contributed to research in topics: Computer science & Hyperspectral imaging. The author has an hindex of 4, co-authored 5 publications receiving 397 citations.

Spectral–spatial classification of hyperspectral images using deep convolutional neural networks

Abstract: In this letter, a novel deep learning framework for hyperspectral image classification using both spectral and spatial features is presented. The framework is a hybrid of principal component analysis, deep convolutional neural networks (DCNNs) and logistic regression (LR). The DCNNs for hierarchically extract deep features is introduced into hyperspectral image classification for the first time. The proposed technique consists of two steps. First, feature map generation algorithm is presented to generate the spectral and spatial feature maps. Second, the DCNNs-LR classifier is trained to get useful high-level features and to fine-tune the whole model. Comparative experiments conducted over widely used hyperspectral data indicate that DCNNs-LR classifier built in this proposed deep learning framework provides better classification accuracy than previous hyperspectral classification methods.

A deep learning framework for hyperspectral image classification using spatial pyramid pooling

Abstract: In this letter, a new deep learning framework for spectral–spatial classification of hyperspectral images is presented. The proposed framework serves as an engine for merging the spatial and spectral features via suitable deep learning architecture: stacked autoencoders (SAEs) and deep convolutional neural networks (DCNNs) followed by a logistic regression (LR) classifier. In this framework, SAEs is aimed to get useful high-level features for the one-dimensional features which is suitable for the dimension reduction of spectral features, while DCNNs can learn rich features from the training data automatically and has achieved state-of-the-art performance in many image classification databases. Though the DCNNs has shown robustness to distortion, it only extracts features of the same scale, and hence is insufficient to tolerate large-scale variance of object. As a result, spatial pyramid pooling (SPP) is introduced into hyperspectral image classification for the first time by pooling the spatial fe...

A Time Varying Ventilation and Dust Control Strategy Based on the Temporospatial Characteristics of Dust Dispersion

Abstract: Generally, an effective ventilation system is essential to reduce coal dust disaster. However, with the implementation of carbon tax and increase of energy and operating costs, it is urgent to design a cost-effective ventilation and dust control system. In this paper, a real driving face in an excavation laneway of coal mine in China was taken as the physical model, and the temporospatial characteristics of airflow and dust dispersion is investigated for the first time to design an original conception of time-varying ventilation and dust control strategy. Specifically, computational fluid dynamic (CFD) approaches are utilized to investigate the dynamic regularity of airflow behavior and dust dispersion, and parametric studies are conducted to select the appropriate ventilation pattern, which is validated to be potential in energy saving as well as dust removal efficiency. In addition, based on the selection of key time point according to the regularity of dust concentration changed over time, the most effective type of speed function is brought out for this novel ventilation system, which reduces energy usage up to 15.11% in a ventilation period. Furthermore, the accuracy of simulation result is verified by field measurements, which demonstrates that adjusting the ventilation velocity at the appropriate time point (case 7) can effectively control the dust concentration, which performs as well as the steady flow. The research results suggest that the further understanding of temporospatial characteristics of dust dispersion is helpful for ventilation design, and significant energy savings and dust removal requirements are verified to be possible in the proposed scheme.

Spectral-Spatial Latent Reconstruction for Open-Set Hyperspectral Image Classification

Abstract: Deep learning-based methods have produced significant gains for hyperspectral image (HSI) classification in recent years, leading to high impact academic achievements and industrial applications. Despite the success of deep learning-based methods in HSI classification, they still lack the robustness of handling unknown object in open-set environment (OSE). Open-set classification is to deal with the problem of unknown classes that are not included in the training set, while in closed-set environment (CSE), unknown classes will not appear in the test set. The existing open-set classifiers almost entirely rely on the supervision information given by the known classes in the training set, which leads to the specialization of the learned representations into known classes, and makes it easy to classify unknown classes as known classes. To improve the robustness of HSI classification methods in OSE and meanwhile maintain the classification accuracy of known classes, a spectral-spatial latent reconstruction framework which simultaneously conducts spectral feature reconstruction, spatial feature reconstruction and pixel-wise classification in OSE is proposed. By reconstructing the spectral and spatial features of HSI, the learned feature representation is enhanced, so as to retain the spectral-spatial information useful for rejecting unknown classes and distinguishing known classes. The proposed method uses latent representations for spectral-spatial reconstruction, and achieves robust unknown detection without compromising the accuracy of known classes. Experimental results show that the performance of the proposed method outperforms the existing state-of-the-art methods in OSE.

Geometric-Spectral Reconstruction Learning for Multi-Source Open-Set Classification With Hyperspectral and LiDAR Data

Abstract: This letter presents an open-set classification method of remote sensing images (RSIs) based on geometric-spectral reconstruction learning. More specifically, in order to improve the ability of RSI classification model to adapt to the open-set environment, an open-set classification method based on geometric and spectral feature fusion is proposed. This method proposes to realize RSI open-set classification based on geometric and spectral features with hyperspectral and light detection and ranging (LiDAR) data for the first time. In a variety of data sources of remote sensing, hyperspectral images (HSIs) and LiDAR data can provide rich spectral and geometric information for target objects. This letter combines both HSIs and LiDAR data to realize the recognition of unknown classes and the classification of known classes. Experiments show that the proposed method is better than previous state-of-the-art methods.

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Abstract: Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. Considering the low-level features (e.g., spectral and texture) as the bottom level, the output feature representation from the top level of the network can be directly fed into a subsequent classifier for pixel-based classification. As a matter of fact, by carefully addressing the practical demands in RS applications and designing the input?output levels of the whole network, we have found that DL is actually everywhere in RS data analysis: from the traditional topics of image preprocessing, pixel-based classification, and target recognition, to the recent challenging tasks of high-level semantic feature extraction and RS scene understanding.

Implementation of machine-learning classification in remote sensing: an applied review

Abstract: Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learning classification is not straightforward, and the literature provides conflicting advice regarding many key issues. This article therefore provides an overview of machine learning from an applied perspective. We focus on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN). Issues considered include the choice of algorithm, training data requirements, user-defined parameter selection and optimization, feature space impacts and reduction, and computational costs. We illustrate these issues through applying machine-learning classification to two publically available remotely sensed dat...

Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach

Abstract: In this paper, we propose a spectral–spatial feature based classification (SSFC) framework that jointly uses dimension reduction and deep learning techniques for spectral and spatial feature extraction, respectively. In this framework, a balanced local discriminant embedding algorithm is proposed for spectral feature extraction from high-dimensional hyperspectral data sets. In the meantime, convolutional neural network is utilized to automatically find spatial-related features at high levels. Then, the fusion feature is extracted by stacking spectral and spatial features together. Finally, the multiple-feature-based classifier is trained for image classification. Experimental results on well-known hyperspectral data sets show that the proposed SSFC method outperforms other commonly used methods for hyperspectral image classification.

Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification

Abstract: We propose an end-to-end framework for the dense, pixelwise classification of satellite imagery with convolutional neural networks (CNNs). In our framework, CNNs are directly trained to produce classification maps out of the input images. We first devise a fully convolutional architecture and demonstrate its relevance to the dense classification problem. We then address the issue of imperfect training data through a two-step training approach: CNNs are first initialized by using a large amount of possibly inaccurate reference data, and then refined on a small amount of accurately labeled data. To complete our framework, we design a multiscale neuron module that alleviates the common tradeoff between recognition and precise localization. A series of experiments show that our networks consider a large amount of context to provide fine-grained classification maps.

Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network

Abstract: Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record.