Endmember Extraction of Hyperspectral Remote Sensing Images Based on the Ant Colony Optimization (ACO) Algorithm

Zhang, Bing; Sun, Xun; Gao, Lianru; Yang, Ling

doi:10.1109/tgrs.2011.2108305

Cited by 118 publications

(66 citation statements)

References 22 publications

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“…Statistical analysis revealed that many of the hyperspectral imagery bands are highly correlated [4,35,36], which means that it is necessary to perform a dimension reduction process on the hyperspectral imagery [31]. The MNF transformation algorithm was widely used in hyperspectral data feature extraction [4,10,12,37,38]. Studies showed that MNFextracted features performed better in hyperspectral data-based image segmentation [26].…”

Section: Image Feature Extractionmentioning

confidence: 99%

Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data

Liu

2015

Remote Sensing

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Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR) data in an object-based image analysis (OBIA) paradigm. The aims of this work were the following: (i) to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii) to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii) to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species' separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI) with a 1 m spatial resolution and the Canopy Height Model (CHM) data derived from the LiDAR data acquired by the airborne OPEN ACCESSRemote Sens. 2015, 7 923Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR), VHR/CHM, and minimum noise fractional transformed data (MNF)/CHM) were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that incorporating the crop height information into the hyperspectral extracted features provided a substantial increase in the classification accuracy. The combination of MNF and CHM produced higher classification accuracy than the combination of VHR and CHM, and the solely MNF-based classification results. The textural and geometric features in the object-based classification could significantly improve the accuracy of the crop species classification. By using the proposed object-based classification framework, a crop species classification result with an overall accuracy of 90.33% and a kappa of 0.89 was achieved in our study area.

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Section: Image Feature Extractionmentioning

confidence: 99%

Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data

Liu

2015

Remote Sensing

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“…Selection of endmembers from spectrally less orthogonal materials on the surface is the most difficult step of the spectral mixture modeling, which involves identifying both the number and type of endmembers with their corresponding spectral signatures (Tompkins et al, 1997;BingZhang et al, 2011;Somers et al, 2011). Different approaches such as; manual selection by selecting the spectra corresponding to the pixel within an image; or from libraries of endmember spectra measured in the laboratory or in the field; or by matching the image spectra using the mixture of library endmembers; or using higher level automatic endmember selection methods, have been proposed to select the optimal number and type of endmembers for a specific scene (Bateson and Curtiss, 1996; Tompkins et al, 1997; Garcia-Haro et al, 1999;Winter, 2007;BingZhang et al, 2011;Somers et al, 2011). Even with the extraction of endmember spectra from the image, which is the easiest mention of endmember selection, there are problems.…”

Section: Spectral-mixture Modelingmentioning

confidence: 99%

Spectral mapping of morphological features on the moon with MGM and SAM

Kodikara¹,

Ray

Chauhan

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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“…SU has two tasks: EE and abundance estimation. It is usually assumed that there are some pixels that contain only one kind of ground object in the image, and EE is to find out such pure pixel for basic ground objects [8]. Abundance estimation is the process to estimate different proportion of each endmember in a mixed pixel.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimized Endmember Extraction for Hyperspectral Image

Liu

Zhang

2017

Remote Sensing

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Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis. It is also a challenging task due to the intrinsic complexity of remote sensing images and the lack of priori knowledge. In recent years, a number of EE methods have been developed, where several different optimization objectives have been proposed from different perspectives. In all of these methods, only one objective function has to be optimized, which represents a specific characteristic of endmembers. However, one single-objective function may not be able to express all the characteristics of endmembers from various aspects, which would not be powerful enough to provide satisfactory unmixing results because of the complexity of remote sensing images. In this paper, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is utilized to tackle the problem of EE, where two objective functions, namely, volume maximization (VM) and root-mean-square error (RMSE) minimization are simultaneously optimized. Experimental results on two real hyperspectral images show the superiority of the proposed MODPSO with respect to the single objective D-PSO method, and MODPSO still needs further improvement on the optimization of the VM with respect to other approaches.

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Endmember Extraction of Hyperspectral Remote Sensing Images Based on the Ant Colony Optimization (ACO) Algorithm

Cited by 118 publications

References 22 publications

Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data

Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data

Spectral mapping of morphological features on the moon with MGM and SAM

Multiobjective Optimized Endmember Extraction for Hyperspectral Image

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