scholarly journals Ensemble-Based Cascaded Constrained Energy Minimization for Hyperspectral Target Detection

2019 ◽  
Vol 11 (11) ◽  
pp. 1310 ◽  
Author(s):  
Rui Zhao ◽  
Zhenwei Shi ◽  
Zhengxia Zou ◽  
Zhou Zhang
Keyword(s):  
Target Detection ◽  
Energy Minimization ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Least Square ◽  
Learning System ◽  
Strong Nonlinearity ◽  
Generalization Ability ◽  
Detection Algorithms ◽  
Ensemble Techniques

Ensemble learning is an important group of machine learning techniques that aim to enhance the nonlinearity and generalization ability of a learning system by aggregating multiple learners. We found that ensemble techniques show great potential for improving the performance of traditional hyperspectral target detection algorithms, while at present, there are few previous works have been done on this topic. To this end, we propose an Ensemble based Constrained Energy Minimization (E-CEM) detector for hyperspectral image target detection. Classical hyperspectral image target detection algorithms like Constrained Energy Minimization (CEM), matched filter (MF) and adaptive coherence/cosine estimator (ACE) are usually designed based on constrained least square regression methods or hypothesis testing methods with Gaussian distribution assumption. However, remote sensing hyperspectral data captured in a real-world environment usually shows strong nonlinearity and non-Gaussianity, which will lead to performance degradation of these classical detection algorithms. Although some hierarchical detection models are able to learn strong nonlinear discrimination of spectral data, due to the spectrum changes, these models usually suffer from the instability in detection tasks. The proposed E-CEM is designed based on the classical CEM detection algorithm. To improve both of the detection nonlinearity and generalization ability, the strategies of “cascaded detection”, “random averaging” and “multi-scale scanning” are specifically designed. Experiments on one synthetic hyperspectral image and two real hyperspectral images demonstrate the effectiveness of our method. E-CEM outperforms the traditional CEM detector and other state-of-the-art detection algorithms. Our code will be made publicly available.

Comparative Studies of Target Detection Algorithm for Hyperspectral Imagery

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
B K Nagesha ◽  
M R Puttaswamy ◽  
Dsouza Hasmitha ◽  
G Hemantha Kumar
Keyword(s):  
Comparative Study ◽  
Hyperspectral Imaging ◽  
Target Detection ◽  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Detection Algorithm ◽  
Hyperspectral Data ◽  
Detection Algorithms ◽  
Complex Process

<p>Target detection in hyperspectral imagery is a complex process due to many factors. Exploiting the hyperspectral image<br />for analysis is very challenging due to large information and low spatial resolution. However, hyperspectral target<br />detection has numerous applications. Hence, it is important to pursue research in target detection. In this paper, a<br />comparative study of target detection algorithms for hyperspectral imagery is presented along with scope for future<br />research. A comparative study behind the hyperspectral imaging is detailed. Also, various challenges involved in<br />exploring the hyperspectral data are discussed.</p>

Hyperspectral data collection for the assessment of target detection algorithms: the Viareggio 2013 trial

2014 ◽  
Author(s):  
Alessandro Rossi ◽  
Nicola Acito ◽  
Marco Diani ◽  
Giovanni Corsini ◽  
Sergio Ugo De Ceglie ◽  
...  
Keyword(s):  
Data Collection ◽  
Target Detection ◽  
Hyperspectral Data ◽  
Detection Algorithms

Study on Anomaly Detection Methods in Hyperspectral Image

2014 ◽  
Vol 631-632 ◽  
pp. 631-635
Author(s):  
Yi Ting Wang ◽  
Shi Qi Huang ◽  
Hong Xia Wang ◽  
Dai Zhi Liu
Keyword(s):  
Anomaly Detection ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Detection Methods ◽  
Imaging Data ◽  
Detection Algorithms ◽  
Sensing Technology ◽  
Anomaly Detector ◽  
And Performance ◽  
Spectral Diagnosis

Hyperspectral remote sensing technology can be used to make a correct spectral diagnosis on substances. So it is widely used in the field of target detection and recognition. However, it is very difficult to gather accurate prior information for target detect since the spectral uncertainty of objects is pervasive in existence. An anomaly detector can enable one to detect targets whose signatures are spectrally distinct from their surroundings with no prior knowledge. It becomes a focus in the field of target detection. Therefore, we study four anomaly detection algorithms and conclude with empirical results that use hyperspectral imaging data to illustrate the operation and performance of various detectors.

Comparative Assessment of Target-Detection Algorithms for Urban Targets Using Hyperspectral Data

2021 ◽  
Vol 87 (5) ◽  
pp. 349-362
Author(s):  
Shalini Gakhar ◽  
K.C. Tiwari
Keyword(s):  
Machine Learning ◽  
Target Detection ◽  
City Planning ◽  
Urban Landscape ◽  
Relevant Information ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Distance Measures ◽  
Information Measures ◽  
Detection Algorithms

Hyperspectral data present better opportunities to exploit the treasure of spectral and spatial content that lies within their spectral bands. Hyperspectral data are increasingly being considered for exploring levels of urbanization, due to their capability to capture the spectral variability that a modern urban landscape offers. Data and algorithms are two sides of a coin: while the data capture the variations, the algorithms provide suitable methods to extract relevant information. The literature reports a variety of algorithms for extraction of urban information from any given data, with varying accuracies. This article aims to explore the binary-classifier approach to target detection to extract certain features. Roads and roofs are the most common features present in any urban scene. These experiments were conducted on a subset of AVIRIS-NG hyperspectral data from the Udaipur region of India, with roads and roofs as targets. Four categories of target-detection algorithms are identified from a literature survey and our previous experience—distance measures, angle-based measures, information measures, and machine-learning measures—followed by performance evaluation. The article also presents a brief taxonomy of algorithms; explores methods such as the Mahalanobis angle, which has been reported to be effective for extraction of urban targets; and explores newer machine-learning algorithms to increase accuracy. This work is likely to aid in city planning, sustainable development, and various other governmental and nongovernmental efforts related to urbanization.

scholarly journals DETECTION OF BUILT-UP AREAS USING POLARIMETRIC SYNTHETIC APERTURE RADAR DATA AND HYPERSPECTRAL IMAGE

2015 ◽  
Vol XL-1-W5 ◽  
pp. 105-110 ◽  
Author(s):  
R. Bordbari ◽  
Y. Maghsoudi ◽  
M. Salehi
Keyword(s):  
Synthetic Aperture Radar ◽  
San Francisco ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Majority Voting ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Synthetic Aperture Radar Data ◽  
Aperture Radar

Polarimetric synthetic aperture radar (POLSAR) is an advantageous data for information extraction about objects and structures by using the wave scattering and polarization properties. Hyperspectral remote sensing exploits the fact that all materials reflect, absorb, and emit electromagnetic energy, at specific wavelengths, in distinctive patterns related to their molecular composition. As a result of their fine spectral resolution, Hyperspectral image (HIS) sensors provide a significant amount of information about the physical and chemical composition of the materials occupying the pixel surface. In target detection applications, the main objective is to search the pixels of an HSI data cube for the presence of a specific material (target). In this research, a hierarchical constrained energy minimization (hCEM) method using 5 different adjusting parameters has been used for target detection from hyperspectral data. Furthermore, to detect the built-up areas from POLSAR data, building objects discriminated from surrounding natural media presented on the scene using Freeman polarimetric target decomposition (PTD) and the correlation coefficient between co-pol and cross-pol channels. Also, target detection method has been implemented based on the different polarization basis for using the more information. Finally a majority voting method has been used for fusing the target maps. The polarimetric image C-band SAR data acquired by Radarsat-2, over San Francisco Bay area was used for the evaluation of the proposed method.

Locality Preserving Projection Based on Endmember Extraction for Hyperspectral Image Dimensionality Reduction and Target Detection

2016 ◽  
Vol 70 (9) ◽  
pp. 1573-1581 ◽  
Author(s):  
Yiting Wang ◽  
Shiqi Huang ◽  
Zhigang Liu ◽  
Hongxia Wang ◽  
Daizhi Liu
Keyword(s):  
Dimensionality Reduction ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Image Data ◽  
Hyperspectral Data ◽  
Detection Accuracy ◽  
Endmember Extraction ◽  
Locality Preserving Projection ◽  
Locality Preserving ◽  
Weighted Matrix

In order to reduce the effect of spectral variability on calculation precision for the weighted matrix in the locality preserving projection (LPP) algorithm, an improved dimensionality reduction method named endmember extraction-based locality preserving projection (EE-LPP) is proposed in this paper. The method primarily uses the vertex component analysis (VCA) method to extract endmember spectra from hyperspectral imagery. It then calculates the similarity between pixel spectra and the endmember spectra by using the spectral angle distance, and uses it as the basis for selecting neighboring pixels in the image and constructs a weighted matrix between pixels. Finally, based on the weighted matrix, the idea of the LPP algorithm is applied to reduce the dimensions of hyperspectral image data. Experimental results of real hyperspectral data demonstrate that the low-dimensional features acquired by the proposed methods can fully reflect the characteristics of the original image and further improve target detection accuracy.

scholarly journals A Novel Analysis Dictionary Learning Model Based Hyperspectral Image Classification Method

2019 ◽  
Vol 11 (4) ◽  
pp. 397 ◽  
Author(s):  
Wei Wei ◽  
Mengting Ma ◽  
Cong Wang ◽  
Lei Zhang ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Image Classification ◽  
Dictionary Learning ◽  
Hyperspectral Image ◽  
Learning Model ◽  
Hyperspectral Data ◽  
Experimental Results ◽  
Classification Method ◽  
Strong Nonlinearity ◽  
Hyperspectral Image Classification

Supervised hyperspectral image (HSI) classification has been acknowledged as one of the fundamental tasks of hyperspectral data analysis. Witnessing the success of analysis dictionary learning (ADL)-based method in recent years, we propose an ADL-based supervised HSI classification method in this paper. In the proposed method, the dictionary is modeled considering both the characteristics within the spectrum and among the spectra. Specifically, to reduce the influence of strong nonlinearity within each spectrum on classification, we divide the spectrum into some segments, and based on this we propose HSI classification strategy. To preserve the relationships among spectra, similarities among pixels are introduced as constraints. Experimental results on several benchmark hyperspectral datasets demonstrate the effectiveness of the proposed method for HSI classification.

A Euclidean Distance based Super Resolution Method for Sub pixel target detection in Hyper Spectral Images

GIS Business ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 104-124
Author(s):  
Dr. K. C. Tiwari ◽  
Amrita Bhandari
Keyword(s):  
Spatial Distribution ◽  
Target Detection ◽  
Euclidean Distance ◽  
Super Resolution ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Resolution Method ◽  
Mixed Pixel ◽  
Detection Algorithms ◽  
Hyper Spectral

Most target detection algorithms suffer from the limitation that they can detect only the full pixels of the target while the target may also reside, besides the full pixel, partially in several surrounding pixels. In some cases, the target may even be embedded completely within the pixel. Both these cases are known as subpixel target detection problem. Many target detection applications, however, require detection of full pixels as well as detection of subpixel targets in the surrounding pixels which constitute a case of the mixed pixel. The problem is addressed by full pixel detection followed by spectral unmixing to determine the abundance fraction of the target. Though spectral unmixing gives the abundance fractions, it still does not give the spatial distribution/ arrangement of subpixels of the target with the surrounding pixels. The process of optimizing the spatial distribution of subpixels inside any given pixel based on the available abundance fractions is known as super resolution. This paper investigates Inverse Euclidean distance based super resolution. The algorithm performs well at different scale factors both for synthetic and real hyperspectral data which can aid the super resolution process significantly and thereby enhance the identification and recognition of target. A comparative assessment is also performed with Pixel Swap algorithm.

scholarly journals Joint Hapke Model and Spatial Adaptive Sparse Representation with Iterative Background Purification for Martian Serpentine Detection

2021 ◽  
Vol 13 (3) ◽  
pp. 500
Author(s):  
Xing Wu ◽  
Xia Zhang ◽  
John Mustard ◽  
Jesse Tarnas ◽  
Honglei Lin ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Energy Minimization ◽  
Infrared Imaging ◽  
Hyperspectral Image ◽  
Matched Filter ◽  
Laboratory Data ◽  
Robust Detection ◽  
Spatially Adaptive ◽  
Representation Method

Visible and infrared imaging spectroscopy have greatly revolutionized our understanding of the diversity of minerals on Mars. Characterizing the mineral distribution on Mars is essential for understanding its geologic evolution and past habitability. The traditional handcrafted spectral index could be ambiguous as it may denote broad mineralogical classes, making this method unsuitable for definitive mineral investigation. In this work, the target detection technique is introduced for specific mineral mapping. We have developed a new subpixel mineral detection method by joining the Hapke model and spatially adaptive sparse representation method. Additionally, an iterative background dictionary purification strategy is proposed to obtain robust detection results. Laboratory hyperspectral image containing Mars Global Simulant and serpentine mixtures was used to evaluate and tailor the proposed method. Compared with the conventional target detection algorithms, including constrained energy minimization, matched filter, hierarchical constrained energy minimization, sparse representation for target detection, and spatially adaptive sparse representation method, the proposed algorithm has a significant improvement in accuracy about 30.14%, 29.67%, 29.41%, 9.13%, and 8.17%, respectively. Our algorithm can detect subpixel serpentine with an abundance as low as 2.5% in laboratory data. Then the proposed algorithm was applied to two well-studied Compact Reconnaissance Imaging Spectrometer for Mars images, which contain serpentine outcrops. Our results are not only consistent with the spatial distribution of Fe/Mg phyllosilicates derived by spectral indexes, but also denote what the specific mineral is. Experimental results show that the proposed algorithm enables the search for subpixel, low-abundance, and scientifically valuable mineral deposits.

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