scholarly journals The Fast Spectral Clustering Based on Spatial Information for Large Scale Hyperspectral Image

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 141045-141054
Author(s):  
Yiwei Wei ◽  
Chao Niu ◽  
Yiting Wang ◽  
Hongxia Wang ◽  
Daizhi Liu
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Spatial Information ◽  
Hyperspectral Image

scholarly journals Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification

2019 ◽  
Vol 11 (4) ◽  
pp. 399 ◽  
Author(s):  
Yang Zhao ◽  
Yuan Yuan ◽  
Qi Wang
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Spectral Clustering ◽  
Large Scale ◽  
Environmental Science ◽  
Hyperspectral Image ◽  
Eigenvalue Decomposition ◽  
Hyperspectral Image Classification ◽  
Approximation Techniques ◽  
Image Datasets

Hyperspectral image classification is a challenging and significant domain in the field of remote sensing with numerous applications in agriculture, environmental science, mineralogy, and surveillance. In the past years, a growing number of advanced hyperspectral remote sensing image classification techniques based on manifold learning, sparse representation and deep learning have been proposed and reported a good performance in accuracy and efficiency on state-of-the-art public datasets. However, most existing methods still face challenges in dealing with large-scale hyperspectral image datasets due to their high computational complexity. In this work, we propose an improved spectral clustering method for large-scale hyperspectral image classification without any prior information. The proposed algorithm introduces two efficient approximation techniques based on Nyström extension and anchor-based graph to construct the affinity matrix. We also propose an effective solution to solve the eigenvalue decomposition problem by multiplicative update optimization. Experiments on both the synthetic datasets and the hyperspectral image datasets were conducted to demonstrate the efficiency and effectiveness of the proposed algorithm.

scholarly journals An Unsupervised Deep Learning System for Acoustic Scene Analysis

2020 ◽  
Vol 10 (6) ◽  
pp. 2076
Author(s):  
Mou Wang ◽  
Xiao-Lei Zhang ◽  
Susanto Rahardja
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Spatial Information ◽  
Learning System ◽  
Scene Analysis ◽  
Acoustic Feature ◽  
Large Scale Data ◽  
Unsupervised Deep Learning ◽  
Audio Data ◽  
Low Dimensional

Acoustic scene analysis has attracted a lot of attention recently. Existing methods are mostly supervised, which requires well-predefined acoustic scene categories and accurate labels. In practice, there exists a large amount of unlabeled audio data, but labeling large-scale data is not only costly but also time-consuming. Unsupervised acoustic scene analysis on the other hand does not require manual labeling but is known to have significantly lower performance and therefore has not been well explored. In this paper, a new unsupervised method based on deep auto-encoder networks and spectral clustering is proposed. It first extracts a bottleneck feature from the original acoustic feature of audio clips by an auto-encoder network, and then employs spectral clustering to further reduce the noise and unrelated information in the bottleneck feature. Finally, it conducts hierarchical clustering on the low-dimensional output of the spectral clustering. To fully utilize the spatial information of stereo audio, we further apply the binaural representation and conduct joint clustering on that. To the best of our knowledge, this is the first time that a binaural representation is being used in unsupervised learning. Experimental results show that the proposed method outperforms the state-of-the-art competing methods.

scholarly journals A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

2021 ◽  
Vol 13 (3) ◽  
pp. 355
Author(s):  
Weixian Tan ◽  
Borong Sun ◽  
Chenyu Xiao ◽  
Pingping Huang ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Sandy Land ◽  
Classification Methods ◽  
The Many ◽  
Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

scholarly journals Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

scholarly journals A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

2021 ◽  
Vol 13 (2) ◽  
pp. 176
Author(s):  
Peng Zheng ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yi Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Retrieval System ◽  
Hyperspectral Image ◽  
Parallel Implementation ◽  
Remotely Sensed Data ◽  
Web Interfaces ◽  
Content Retrieval ◽  
Service Mode ◽  
Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

Out in the wash: spatial ecology of a temperate marine shallow rocky-reef species derived using acoustic telemetry

2015 ◽  
Vol 66 (6) ◽  
pp. 559 ◽  
Author(s):  
Jerom R. Stocks ◽  
Charles A. Gray ◽  
Matthew D. Taylor
Keyword(s):  
Spatial Ecology ◽  
Large Scale ◽  
Spatial Information ◽  
Activity Space ◽  
Rocky Reef ◽  
Temperate Reef ◽  
Detection Frequency ◽  
Detection Depth ◽  
Eastern Australia ◽  
Temperate Reef Fish

Characterising the movement and habitat affinities of fish is a fundamental component in understanding the functioning of marine ecosystems. A comprehensive array of acoustic receivers was deployed at two near-shore coastal sites in south-eastern Australia, to examine the movements, activity-space size and residency of a temperate rocky-reef, herbivorous species Girella elevata. Twenty-four G. elevata individuals were internally tagged with pressure-sensing acoustic transmitters across these two arrays and monitored for up to 550 days. An existing network of coastal receivers was used to examine large-scale movement patterns. Individuals exhibited varying residency, but all had small activity-space sizes within the arrays. The species utilised shallow rocky-reef habitat, displaying unimodal or bimodal patterns in depth use. A positive correlation was observed between wind speed and the detection depth of fish, with fish being likely to move to deeper water to escape periods of adverse conditions. Detection frequency data, corrected using sentinel tags, generally illustrated diurnal behaviour. Patterns of habitat usage, residency and spatial utilisation highlighted the susceptibility of G. elevata to recreational fishing pressure. The results from the present study will further contribute to the spatial information required in the zoning of effective marine protected areas, and our understanding of temperate reef fish ecology.

scholarly journals Semantic overlay network for large-scale spatial information indexing

2013 ◽  
Vol 57 ◽  
pp. 208-217 ◽  
Author(s):  
Zhiqiang Zou ◽  
Yue Wang ◽  
Kai Cao ◽  
Tianshan Qu ◽  
Zhongmin Wang
Keyword(s):  
Large Scale ◽  
Spatial Information ◽  
Overlay Network
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