Pavia Center Research Articles

A Spectral-Spatial Cascaded 3D Convolutional Neural Network with a Convolutional Long Short-Term Memory Network for Hyperspectral Image Classification

Deep learning methods used for hyperspectral image (HSI) classification often achieve greater accuracy than traditional algorithms but require large numbers of training epochs. To simplify model structures and reduce their training epochs, an end-to-end deep learning framework incorporating a spectral-spatial cascaded 3D convolutional neural network (CNN) with a convolutional long short-term memory (CLSTM) network, called SSCC, is proposed herein for HSI classification. The SSCC framework employs cascaded 3D CNN to learn the spectral-spatial features of HSIs and uses the CLSTM network to extract sequence features. Residual connections are used in SSCC to accelerate model convergence, with the outputs of previous convolutional layers concatenated as inputs for subsequent layers. Moreover, the data augmentation, parametric rectified linear unit, dynamic learning rate, batch normalization, and regularization (including dropout and L2) methods are used to increase classification accuracy and prevent overfitting. These attributes allow the SSCC framework to achieve good performance for HSI classification within 20 epochs. Three well-known datasets including Indiana Pines, University of Pavia, and Pavia Center were employed to evaluate the classification performance of the proposed algorithm. The GF-5 dataset of Anxin County, obtained from China’s recently launched spaceborne Advanced Hyperspectral Imager, was also used for classification experiments. The experimental results demonstrate that the proposed SSCC framework achieves state-of-the-art performance with better training efficiency than other deep learning methods.

Multitask Deep Learning With Spectral Knowledge for Hyperspectral Image Classification

In this letter, we propose a multitask deep learning method for classification of multiple hyperspectral data in a single training. Deep learning models have achieved promising results on hyperspectral image classification, but their performance highly rely on sufficient labeled samples, which are scarce on hyperspectral images. However, samples from multiple data sets might be sufficient to train one deep learning model, thereby improving its performance. To do so, we trained an identical feature extractor for all data, and the extracted features were fed into corresponding Softmax classifiers. Spectral knowledge was introduced to ensure that the shared features were similar across domains. Four hyperspectral data sets were used in the experiments. We achieved higher classification accuracies on three data sets (Pavia University, Pavia Center, and Indian Pines) and competitive results on the Salinas Valley data compared with the baseline. Spectral knowledge was useful to prevent the deep network from overfitting when the data shared similar spectral response. The proposed method tested on two deep CNNs successfully shows its ability to utilize samples from multiple data sets and enhance networks' performance.

Sequential spectral clustering of hyperspectral remote sensing image over bipartite graph

Unsupervised classification is a crucial step in remote sensing hyperspectral image analysis where producing labeled data is a laborious task. Spectral Clustering is an appealing graph-partitioning technique with outstanding performance on data with non-linear dependencies. However, Spectral Clustering is restricted to small-scale data and neither has been effectively applied to hyperspectral image analysis. In this paper, the unsupervised classification of hyperspectral images is addressed through a sequential spectral clustering that can be extended to the large-scale hyperspectral image. To this end, this paper utilizes a bipartite graph representation along with a sequential singular value decomposition and mini-batch K-means for unsupervised classification of hyperspectral imagery. We evaluate the proposed algorithm with several benchmark hyperspectral datasets including Botswana, Salinas, Indian Pines, Pavia Center Scene and Pavia University Scene. The experimental results show significant improvements made by the proposed algorithm compared to the state-of-art clustering algorithms.

Convolutional neural network extreme learning machine for effective classification of hyperspectral images

Due to its excellent performance in terms of fast implementation, strong generalization capability and straightforward solution, extreme learning machine (ELM) has attracted increasingly attentions in pattern recognition such as face recognition and hyperspectral image (HSI) classification. However, the performance of ELM for HSI classification remains a challenging problem especially in effective extraction of the featured information from the massive volume of data. To this end, we propose in this paper a new method to combine Convolutional neural network (CNN) with ELM (CNN-ELM) for HSI classification. As CNN has been successfully applied for feature extraction in different applications, the combined CNN-ELM approach aims to take advantages of these two techniques for improved classification of HSI. By preserving the spatial features whilst reconstructing the spectral features of HSI, the proposed CNN-ELM method can significantly improve the accuracy of HSI classification without increasing the computational complexity. Comprehensive experiments using three publicly available HSI data sets, Pavia University, Pavia center, and Salinas have fully validated the improved performance of the proposed method when benchmarking with several state-of-the-art approaches.

A Binary Social Spider Optimization algorithm for unsupervised band selection in compressed hyperspectral images

The presence of high spectral correlation in hyperspectral images has demanded dimensionality reduction. Selection of bands based on supervised techniques has been already developed for dimensionality reduction. In this manuscript, the selection of an optimal set of bands is formulated as an unsupervised problem. The images are initially compressed using discrete wavelet transform (DWT). Then, the details of the image are extracted in the transform domain using entropy which accounts for spatial information content and first spectral derivative (FSD) that exploits the temporal redundancy. These details are optimized using meta-heuristic algorithms to obtain the final set of bands. A new Binary Social Spider Optimization algorithm (BSSO) is proposed which maintains the exploration–exploitation balance and avoids premature convergence of solutions. The performance of BSSO is compared with other benchmark algorithms: Clonal algorithm (BCLONAL), Time-varying transfer function Binary Particle Swarm Optimization (TV-BPSO), Binary Genetic Algorithm (BGA), Improved Gravitational Search Algorithm (IBGSA) and New Binary Bat Algorithm (NBBA). A simulation study is carried on four standard hyperspectral images. The proposed BSSO have provided with 83% of overall accuracy for 24% bands in Salinas image set, 75% for 33% in Pavia University, 75% against 26% for Pavia Center and finally 91% in KSC with 32% bands retention. The achievement of higher accuracy at less than 50% bands is appreciable.

Channel estimation, equalisation, and evaluation for high‐mobility airborne hyperspectral data transmission

In the past few years, unmanned aerial vehicles (UAVs) have become a primary airborne platform for hyperspectral imager for studies on precision agriculture, defence, and the environment. The ‘push-broom’ type of hyperspectral sensors require moving vehicle, and transmission and analysis of hyperspectral data by means of a UAV's high-mobility channel is challenging. While high bandwidth of hyperspectral imaging justify using orthogonal frequency division multiplexing (OFDM) for data transmission, the high speed of UAVs imposes intercarrier interference (ICI) on the transmitted OFDM signal because of the Doppler shift. This study proposes a technique for channel estimation and equalisation in order to compensate the ICI. This technique uses a complete channel matrix estimation in the frequency domain in contrast to conventional methods that only use diagonal elements when recovering the data. In order to evaluate the received data using this technique, a classification framework was designed that took into consideration both spectral and spatial information. In order to verify the robustness of the proposed model, the system was analysed using a Pavia Center hyperspectral dataset, and evaluated against speeds of 50 and 500 m/s. By using this method, improvement in both data transmission and the analysis was achieved.

Bayesian classification of hyperspectral images using spatially-varying Gaussian mixture model

We propose a spatially-varying Gaussian mixture model for joint spectral and spatial classification of hyperspectral images. The model provides a robust estimation framework for small sample size training sets. Defining prior distributions for the mean vector and the covariance matrix enables us to regularize the parameter estimation problem. More specifically, we can obtain invertible positive definite covariance matrices by the help of this regularization. Moreover, the proposed model also takes into account the spatial alignments of the pixels by using spatially-varying mixture proportions. The spatially-varying mixture model is based on spatial multinomial logistic regression. The classification results obtained on Indian Pines, Pavia Centre, Pavia University, and Salinas data sets show that the proposed methods perform better especially for small-sized training sets compared to the state-of-the-art classifiers.

A Local Search-Based GeneSIS algorithm for the Segmentation and Classification of Remote-Sensing Images

A local search-based version of the so-called genetic sequential image segmentation (GeneSIS) algorithm is presented in this paper, for the classification of remotely sensed images. The new method combines the properties of the GeneSIS framework with the principles of the region growing segmentation algorithms. Localized GeneSIS operates on a fine-segmented image obtained after preliminary watershed transformation. Segmentation proceeds by iterative expansions emanating from object cores, i.e., connected components of marked watersheds. At each expansion trial, the process involves three successively performed operations: 1) generation of the object’s neighborhood to a specified order; 2) local exploration of the neighborhood through an evolutionary algorithm to identify the best expansion to be merged; and 3) rearrangement of the object neighborhoods. We propose two priority strategies for the selection of the objects to be expanded and two different modes of operation performing either supervised or semisupervised segmentation of the image. The combination of the priority strategies and segmentation modes lead to four different implementations of localized GeneSIS. Due to the local search approach adopted here, the resulting algorithms have considerably lower execution times, while at the same time, they provide comparable classification accuracies compared to those produced by previous GeneSIS variants. Experimental analysis is conducted using a hyperspectral forest image, a multispectral agricultural image, and the Pavia Centre image over an urban area. Comparative results are also provided with existing segmentation algorithms.

Misdiagnosis of High Function Autism Spectrum Disorders in Adults: An Italian Case Series

High Function (HF) Autism Spectrum Disorders (ASD) in adulthood is highly prevalent but insufficiently recognized. In Italy, in particular, awareness of this condition is still insufficient and many psychiatrists have no cases of HF ASD to mention. Adult patients with HF ASD come to the attention of Mental Health Services complaining of difficulties within their social context and interpersonal relationships. Objectives: Describe emblematic clinical examples of misdiagnosed HF ASD to understand reasons that conducted to misdiagnosis. Procedure: We contact five specialized Italian Center in diagnosis of ASD. Each center have to describe two or three emblematic cases of adult patient with diagnosis of ASD validated by ADOS-4 but referred to clinicians with another diagnosis, discussing about possible reasons of misdiagnosis. Sample and Results: We have collected 12 case reports (2 from Bologna center, 3 from Torino center, 3 from Pavia center, 2 from Verona center and 2 from Catania center) of adult HF ASD previously misdiagnosed. These cases shows important similarity across centers and highlight that if are taken into account only problems or symptoms that conduct patients to ask help, cases can easily suggest other psychiatric or personality disorders. Diagnosis becomes clear only after considering all the clinical features and a detailed developmental history. Conclusion: Psychiatrists who have insufficient experience of ASD may overlook some symptoms of the overall clinical picture and misdiagnose ASD as personality disorders, schizophrenia, phobia or even as a non-psychiatric condition, so is hopeful for future increased knowledge about HF ASD in adulthood.

BOUNDARY BASED SUPERVISED CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

Abstract. One of the most important and challenging problems in supervised classification of high dimensional data is limited available training samples. Using the parametric classifiers is not appropriate in this condition. Thus a new simple nonparametric supervised classifier based boundary samples of each class is proposed in this paper that need no statistic parameter for classification. Accuracy and reliability of this classifier is compared whit other non-parametric classifiers such as Parallelepiped (box), K nearest neighbours (KNN), Artifical Neural network (ANN) and SVM and also a parametric classifier that use only first order statistic, Minimum Euclidean Distance (MED), for different four datasets, AVARIS data, Pavia University, Pavia center and Salinas data. The results of experiments show that proposed classifier in despite of simplicity has appropriate and reasonable efficiency.

Comparison of Vector Stacking, Multi-SVMs Fuzzy Output, and Multi-SVMs Voting Methods for Multiscale VHR Urban Mapping

The objective of this letter is to integrate multiscale information for urban mapping using very high resolution (VHR) imagery. Three multiscale fusion methods were presented: 1) vector stacking (VS); 2) multiple support vector machines (multi-SVMs) fuzzy output; and 3) multi-SVMs voting. Two kinds of spatial features were used to obtain multiscale representations of VHR images: morphological structural features and object-based approaches. In experiments, the Reflective Optics System Imaging Spectrometer-03 Pavia Center and University, the Hyperspectral Digital Imagery Collection Experiment Washington DC Mall, and the Quickbird Beijing data sets were used for algorithm validation. The experimental results revealed that, in most cases, the VS fusion outperformed other methods because it was able to create a new high-dimensional multiscale feature space and enhance the class separability. It was also shown that the multi-SVMs fuzzy fusion could optimize and reorganize the multiscale information effectively. Furthermore, multi-SVMs fuzzy output was better than multi-SVMs voting because the former was able to exploit the probabilistic output, while the latter only considered the crisp classification label. In addition, it is suggested that VS fusion is suitable for morphological features; however, for the object-based classification, the multiscale fusion methods do not necessarily yield better results than the single-scale classification in terms of accuracies.

Histiocytic activation following neutron irradiation of boron-enriched rat liver metastases.

Annals of the New York Academy of SciencesVolume 832, Issue 1 p. 274-278 Histiocytic Activation following Neutron Irradiation of Boron-Enriched Rat Liver Metastases R. NANO, Corresponding Author R. NANO Department of Animal Biology University of Pavia Center of Study for Histochemistry CNR Pavia, Italy Address correspondence to: Prof. Rosanna Nano, Department of Animal Biology, Piazza Botta 10, University of Pavia, 27100, Pavia, Italy. Telephone, 0382-506315; Fax, 0382-506406; e-mail, [email protected]Search for more papers by this authorS. BARNI, S. BARNI Department of Animal Biology University of Pavia Center of Study for Histochemistry CNR Pavia, ItalySearch for more papers by this authorG. GERZELI, G. GERZELI Department of Animal Biology University of Pavia Center of Study for Histochemistry CNR Pavia, ItalySearch for more papers by this authorT. PINELLI, T. PINELLI Department of Nuclear and Theoretical Physics University of Pavia I.N.F.N. Pavia, ItalySearch for more papers by this authorS. ALTIERI, S. ALTIERI Department of Nuclear and Theoretical Physics University of Pavia I.N.F.N. Pavia, ItalySearch for more papers by this authorF. FOSSATI, F. FOSSATI Department of Nuclear and Theoretical Physics University of Pavia I.N.F.N. Pavia, ItalySearch for more papers by this authorU. PRATI, U. PRATI Department of Surgery Division of General Surgery University of Pavia Pavia, ItalySearch for more papers by this authorL. ROVEDA, L. ROVEDA Department of Surgery Division of General Surgery University of Pavia Pavia, ItalySearch for more papers by this authorA. ZONTA, A. ZONTA Department of Surgery Division of General Surgery University of Pavia Pavia, ItalySearch for more papers by this author R. NANO, Corresponding Author R. NANO Department of Animal Biology University of Pavia Center of Study for Histochemistry CNR Pavia, Italy Address correspondence to: Prof. Rosanna Nano, Department of Animal Biology, Piazza Botta 10, University of Pavia, 27100, Pavia, Italy. Telephone, 0382-506315; Fax, 0382-506406; e-mail, [email protected]Search for more papers by this authorS. BARNI, S. BARNI Department of Animal Biology University of Pavia Center of Study for Histochemistry CNR Pavia, ItalySearch for more papers by this authorG. GERZELI, G. GERZELI Department of Animal Biology University of Pavia Center of Study for Histochemistry CNR Pavia, ItalySearch for more papers by this authorT. PINELLI, T. PINELLI Department of Nuclear and Theoretical Physics University of Pavia I.N.F.N. Pavia, ItalySearch for more papers by this authorS. ALTIERI, S. ALTIERI Department of Nuclear and Theoretical Physics University of Pavia I.N.F.N. Pavia, ItalySearch for more papers by this authorF. FOSSATI, F. FOSSATI Department of Nuclear and Theoretical Physics University of Pavia I.N.F.N. Pavia, ItalySearch for more papers by this authorU. PRATI, U. PRATI Department of Surgery Division of General Surgery University of Pavia Pavia, ItalySearch for more papers by this authorL. ROVEDA, L. ROVEDA Department of Surgery Division of General Surgery University of Pavia Pavia, ItalySearch for more papers by this authorA. ZONTA, A. ZONTA Department of Surgery Division of General Surgery University of Pavia Pavia, ItalySearch for more papers by this author First published: 17 December 2006 https://doi.org/10.1111/j.1749-6632.1997.tb46254.xCitations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume832, Issue1Phagocytes: Biology, Physiology, Pathology, and PharmacotherapeuticsDecember 1997Pages 274-278 RelatedInformation