scholarly journals Hybrid Depth-Separable Residual Networks for Hyperspectral Image Classification

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Cuijie Zhao ◽  
Hongdong Zhao ◽  
Guozhen Wang ◽  
Hong Chen
Keyword(s):  
Hyperspectral Image ◽  
Computational Cost ◽  
Ground Truth ◽  
Hyperspectral Data ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Network ◽  
Benchmark Datasets ◽  
Deep Convolutional Network ◽  
3D Cnn

At present, the classification of the hyperspectral image (HSI) based on the deep convolutional network has made great progress. Due to the high dimensionality of spectral features, limited samples of ground truth, and high nonlinearity of hyperspectral data, effective classification of HSI based on deep convolutional neural networks is still difficult. This paper proposes a novel deep convolutional network structure, namely, a hybrid depth-separable residual network, for HSI classification, called HDSRN. The HDSRN model organically combines 3D CNN, 2D CNN, multiresidual network ROR, and depth-separable convolutions to extract deeper abstract features. On the one hand, due to the addition of multiresidual structures and skip connections, this model can alleviate the problem of over fitting, help the backpropagation of gradients, and extract features more fully. On the other hand, the depth-separable convolutions are used to learn the spatial feature, which reduces the computational cost and alleviates the decline in accuracy. Extensive experiments on the popular HSI benchmark datasets show that the performance of the proposed network is better than that of the existing prevalent methods.

scholarly journals AN UNSUPERVISED LABELING APPROACH FOR HYPERSPECTRAL IMAGE CLASSIFICATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 407-415
Author(s):  
J. González Santiago ◽  
F. Schenkel ◽  
W. Gross ◽  
W. Middelmann
Keyword(s):  
Hyperspectral Image ◽  
Ground Truth ◽  
Current Approach ◽  
Hyperspectral Data ◽  
Agglomerative Clustering ◽  
Hyperspectral Image Analysis ◽  
Hierarchical Agglomerative Clustering ◽  
Benchmark Datasets ◽  
Labeling Approach ◽  
Class Labels

Abstract. The application of hyperspectral image analysis for land cover classification is mainly executed in presence of manually labeled data. The ground truth represents the distribution of the actual classes and it is mostly derived from field recorded information. Its manual generation is ineffective, tedious and very time-consuming. The continuously increasing amount of proprietary and publicly available datasets makes it imperative to reduce these related costs. In addition, adequately equipped computer systems are more capable of identifying patterns and neighbourhood relationships than a human operator. Based on these facts, an unsupervised labeling approach is presented to automatically generate labeled images used during the training of a convolutional neural network (CNN) classifier. The proposed method begins with the segmentation stage where an adapted version of the simple linear iterative clustering (SLIC) algorithm for dealing with hyperspectral data is used. Consequently, the Hierarchical Agglomerative Clustering (HAC) and Fuzzy C-Means (FCM) algorithms are employed to efficiently group similar superpixels considering distances with respect to each other. The distinct utilization of these clustering techniques defines a complementary stage for overcoming class overlapping during image generation. Ultimately, a CNN classifier is trained using the computed image to pixel-wise predict classes on unseen datasets. The labeling results, obtained using two hyperspectral benchmark datasets, indicate that the current approach is able to detect objects boundaries, automatically assign class labels to the entire dataset and to classify new data with a prediction certainty of 90%. Additionally, this method is also capable of achieving better classification accuracy and visual correspondence with reality than the ground truth images.

scholarly journals Adaptable Convolutional Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (18) ◽  
pp. 3637
Author(s):  
Mercedes E. Paoletti ◽  
Juan M. Haut
Keyword(s):  
High Performance ◽  
Hyperspectral Image ◽  
Filter Design ◽  
Computational Cost ◽  
Hyperspectral Data ◽  
Imaging Data ◽  
Convolutional Network ◽  
Spatial Features ◽  
Convolution Model ◽  
Convolution Kernels

Nowadays, a large number of remote sensing instruments are providing a massive amount of data within the frame of different Earth Observation missions. These instruments are characterized by the wide variety of data they can collect, as well as the impressive volume of data and the speed at which it is acquired. In this sense, hyperspectral imaging data has certain properties that make it difficult to process, such as its large spectral dimension coupled with problematic data variability. To overcome these challenges, convolutional neural networks have been proposed as classification models because of their ability to extract relevant spectral–spatial features and learn hidden patterns, along their great architectural flexibility. Their high performance relies on the convolution kernels to exploit the spatial relationships. Thus, filter design is crucial for the correct performance of models. Nevertheless, hyperspectral data may contain objects with different shapes and orientations, preventing filters from “seeing everything possible” during the decision making. To overcome this limitation, this paper proposes a novel adaptable convolution model based on deforming kernels combined with deforming convolution layers to fit their effective receptive field to the input data. The proposed adaptable convolutional network (named DKDCNet) has been evaluated over two well-known hyperspectral scenes, demonstrating that it is able to achieve better results than traditional strategies with similar computational cost for HSI classification.

SGAN4AbSum: A Semantic-Enhanced Generative Adversarial Network for Abstractive Text Summarization

2021 ◽  
Author(s):  
Tham Vo
Keyword(s):  
Ground Truth ◽  
Text Summarization ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Training Strategy ◽  
Adversarial Network ◽  
Deep Recurrent Neural Network ◽  
Benchmark Datasets ◽  
Latent Representations ◽  
Abstractive Summarization

Abstract In abstractive summarization task, most of proposed models adopt the deep recurrent neural network (RNN)-based encoder-decoder architecture to learn and generate meaningful summary for a given input document. However, most of recent RNN-based models always suffer the challenges related to the involvement of much capturing high-frequency/reparative phrases in long documents during the training process which leads to the outcome of trivial and generic summaries are generated. Moreover, the lack of thorough analysis on the sequential and long-range dependency relationships between words within different contexts while learning the textual representation also make the generated summaries unnatural and incoherent. To deal with these challenges, in this paper we proposed a novel semantic-enhanced generative adversarial network (GAN)-based approach for abstractive text summarization task, called as: SGAN4AbSum. We use an adversarial training strategy for our text summarization model in which train the generator and discriminator to simultaneously handle the summary generation and distinguishing the generated summary with the ground-truth one. The input of generator is the jointed rich-semantic and global structural latent representations of training documents which are achieved by applying a combined BERT and graph convolutional network (GCN) textual embedding mechanism. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed SGAN4AbSum which achieve the competitive ROUGE-based scores in comparing with state-of-the-art abstractive text summarization baselines.

scholarly journals Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5684
Author(s):  
Laura Bianca Bilius ◽  
Ştefan Gheorghe Pentiuc
Keyword(s):  
Heuristic Method ◽  
Voronoi Diagrams ◽  
Ground Truth ◽  
Unsupervised Classification ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Spectral Signature ◽  
Area Of Interest ◽  
Parafac Decomposition

Hyperspectral images (HSIs) are a powerful tool to classify the elements from an area of interest by their spectral signature. In this paper, we propose an efficient method to classify hyperspectral data using Voronoi diagrams and strong patterns in the absence of ground truth. HSI processing consumes a great deal of computing resources because HSIs are represented by large amounts of data. We propose a heuristic method that starts by applying Parafac decomposition for reduction and to construct the abundances matrix. Furthermore, the representative nodes from the abundances map are searched for. A multi-partition of these nodes is found, and based on this, strong patterns are obtained. Then, based on the hierarchical clustering of strong patterns, an optimum partition is found. After strong patterns are labeled, we construct the Voronoi diagram to extend the classification to the entire HSI.

scholarly journals A Efficient Solution for Classification of Crops using Hyper Spectral Satellite Images

2019 ◽  
Vol 9 (2) ◽  
pp. 5204-5211
Keyword(s):  
Hyperspectral Image ◽  
Satellite Image ◽  
Hyperspectral Data ◽  
Classification Model ◽  
Research Activity ◽  
Research Issues ◽  
Computational Overhead ◽  
Wide Range ◽  
Crop Classification

Crop identification (CI) utilizing hyperspectral pictures/images (HSI) collected from satellite is one of the effective research area considering various agriculture related applications. Wide range of research activity is carried out and modelled in the area of crop recognition (CR) for building efficient model. Correlation filter (CF) is considered to be one of an effective method and are been applied by existing methodologies for identifying similar signal features. Nonetheless, very limited is work is carried out using CF for crop classification using hyperspectral data. Further, effective method is required that bring good tradeoffs between memory and computational overhead. The crop classification model can be improved by combining machine learning (ML) technique with CF. HSI is composed of hundreds of channels with large data dimension that gives entire information of imaging. Thus, using classification model is very useful for real-time application uses. However, the accuracy of classification task is affected as HSI is composed of high number of redundant and correlated feature sets. Along with, induce computational overhead with less benefits using redundant features. Thus, effective band selection, texture analysis, and classification method is required for accurately classifying multiple crops. This paper analyses various existing techniques for identification and classification of crops using satellite imagery detection method. Then, identify the research issues, challenges, and problems of existing model for building efficient techniques for identification and classification of crops using satellite image. Experiment are conducted on standard hyperspectral data. The result attained shows proposed model attain superior classification accuracy when compared with existing hyperspectral image classification model.

scholarly journals HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS

2015 ◽  
Vol XL-3/W2 ◽  
pp. 169-175 ◽  
Author(s):  
W. Pervez ◽  
S. A. Khan ◽  
Valiuddin
Keyword(s):  
Machine Learning ◽  
Vegetation Index ◽  
Hyperspectral Image ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Supervised Machine Learning ◽  
Spectral Signature ◽  
Processing Application ◽  
Significant Difference

Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is held in first 10 PCs. Feature extraction is one of the important application by using vegetation delineation and normalized difference vegetation index. The machine learning classifiers uses the technique to identify the pixels having significant difference in the spectral signature which is very useful for classification of an image. Supervised machine learning classifier technique has been used for classification of hyperspectral image which resulted in overall efficiency of 86.6703 and Kappa co-efficient of 0.7998.

scholarly journals UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hidetoshi Urakubo ◽  
Torsten Bullmann ◽  
Yoshiyuki Kubota ◽  
Shigeyuki Oba ◽  
Shin Ishii
Keyword(s):  
Image Segmentation ◽  
3D Reconstruction ◽  
Three Dimensional ◽  
Ground Truth ◽  
Rapid Expansion ◽  
Deep Convolutional Neural Networks ◽  
Electron Microscopic ◽  
Ground Truth Generation ◽  
3D Cnn ◽  
2D And 3D

AbstractRecently, there has been rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from stacks of two-dimensional (2D) electron microscopy (EM) images. The spatial scale of the 3D reconstruction increases rapidly owing to deep convolutional neural networks (CNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for CNN-based segmentation. However, the complexity of such pipelines makes their use difficult even for computer experts and impossible for non-experts. In this study, we developed a new software program, called UNI-EM, for 2D and 3D CNN-based segmentation. UNI-EM is a software collection for CNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM incorporates a set of 2D CNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D CNN-based neuron segmentation algorithm. The 2D- and 3D-CNNs are known to demonstrate state-of-the-art level segmentation performance. We then provided two example workflows: mitochondria segmentation using a 2D CNN and neuron segmentation using FFNs. By following these example workflows, users can benefit from CNN-based segmentation without possessing knowledge of Python programming or CNN frameworks.

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