Modified N-FINDR endmember extraction algorithm for remote-sensing imagery

Ji, Liu; Geng, Xiurui; Sun, Kang; Zhao, Yongchao; Gong, Peng

doi:10.1080/01431161.2015.1034895

Cited by 15 publications

(6 citation statements)

References 28 publications

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“…As illustrated in Figure 3a, a water-land mixing model assumes that a mixed pixel is a linear combination of water and land endmembers weighted by their corresponding fractions, which satisfy the abundance sum-to-one constraint (ASC) and abundance nonnegative constraint (ANC) [28], where r is the spectrum of the mixed pixel, eW and eL correspond to the water and land endmembers, and cW and cL represent the fractions of water and land endmembers. The endmembers can be extracted from the Landsat images using the common endmember extraction algorithms [28][29][30][31][32][33]. However, these algorithms often assume that all pixels in the image are constructed by the same mixing model.…”

Section: Solving the Spectral Mixing Problem Using Local Spectral Unmmentioning

confidence: 99%

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

Gong

Geng

et al. 2015

Remote Sensing

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Abstract:The finer resolution observation and monitoring of the global land cover (FROM-GLC) product makes it the first 30 m resolution global land cover product from which one can extract a global water mask. However, two major types of misclassification exist with this product due to spectral similarity and spectral mixing. Mountain and cloud shadows are often incorrectly classified as water since they both have very low reflectance, while more water pixels at the boundaries of water bodies tend to be misclassified as land. In this paper, we aim to improve the accuracy of the 30 m FROM-GLC water mask by addressing those two types of errors. For the first, we adopt an object-based method by computing the topographical feature, spectral feature, and geometrical relation with cloud for every water object in the FROM-GLC water mask, and set specific rules to determine whether a water object is misclassified. For the second, we perform a local spectral unmixing using a two-endmember linear mixing model for each pixel falling in the water-land boundary zone that is 8-neighborhood connected to water-land boundary pixels. Those pixels with big enough water fractions are determined as water. The procedure is automatic. Experimental results show that the total area of inland water has been decreased by 15.83% in the new global water mask compared with the FROM-GLC water mask. Specifically, OPEN ACCESSRemote Sens. 2015, 7 13508 more than 30% of the FROM-GLC water objects have been relabeled as shadows, and nearly 8% of land pixels in the water-land boundary zone have been relabeled as water, whereas, on the contrary, fewer than 2% of water pixels in the same zone have been relabeled as land. As a result, both the user's accuracy and Kappa coefficient of the new water mask (UA = 88.39%, Kappa = 0.87) have been substantially increased compared with those of the FROM-GLC product (UA = 81.97%, Kappa = 0.81).

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Section: Solving the Spectral Mixing Problem Using Local Spectral Unmmentioning

confidence: 99%

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

Gong

Geng

et al. 2015

Remote Sensing

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“…7 shows the synthetic hyperspectral image, Fig. 8 and Table II show the quantitative results by using SAM [25], spectral information divergence (SID) [26] and correlation coefficient (CC) [27], and our method is compared with Vertex Component Analysis (VCA) [28], N-FINDR [29], Simplex Identification via Split Augmented Lagrangian (SISAL) [30], Non-negative Matrix Factorization (NMF) (NMF) [31]. III show the abundance evaluation results of various methods, measured from CC [27], Abundance Information Divergence (AID) [32] and Abundance Angle Distance (AAD) [33].…”

Section: A Simulated Experimental Resultsmentioning

confidence: 99%

Collaborative Coupled Hyperspectral Unmixing Based Subpixel Change Detection for Analyzing Coastal Wetlands

Chang

Meng

Sun

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Owing to the complicated and heterogeneous distribution characteristics of wetland features, the existing hyperspectral technology is difficult to investigate the inner-pixel subtle changes. In this paper, we present a sub-pixel change detection method based on collaborative coupled unmixing (SCDUM) for monitoring coastal wetlands. A novel multitemporal and spatial scale collaborative endmember extraction method based on joint spatial and spectral information is proposed. In the proposed method, the multitemporal hyperspectral images are firstly jointly clustered and segmented based on multi-feature fusion of spectral features, texture features, and shape features. Then a different spatial scale non-negative matrix factorization based on original and down-sampled multitemporal hyperspectral images is proposed to accurately extract the pure endmembers of each segmented images. Finally, the global abundance of the multitemporal image is effectively estimated for change detection. In addition, in order to verify the accuracy of the change detection results without reference, an accuracy verification strategy by using high spatial resolution Sentinel-2A image as auxiliary data is implemented. The Yellow River Estuary coastal wetlands was selected as the research area, and the Gaofen (GF)-5 and ZY-1 02D hyperspectral images were used as the research data. In particular, the proposed method not only provides the overall change information, but also obtains the component of change direction and intensity of each kind of endmember, and the experimental results shows that the SCDUM gives more accurate detection results, with closer to the endmember spectral curves of real objects, compared with other state-of-the-art methods.

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“…Endmembers play the most important role in conducting spectral mixture analysis. Spectral mixture analysis assumes that the spectra measured by sensors for a pixel is a linear combination of the spectra of all components within the pixel (Keshava and Mustard, 2002). This technique is widely used to resolve spectral mixture problems in image analysis (Foody and Cox, 1994;Lu et al, 2003;Wu, 2004;Iordache et al, 2011).…”

Section: Waveform Mixture Algorithmmentioning

confidence: 99%

“…Spectral mixture analysis based on the assumption that the spectra measured by sensors for a pixel are a linear combination of the spectra for all components within the pixel (Keshava and Mustard, 2002) was first applied to the altimetry research field in the polar regions by Chase and Holyer (1990). They estimated sea ice type and concentration using spectral mixture analysis based on Geosat waveforms.…”

Section: Introductionmentioning

confidence: 99%

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

Lee

Kim

2018

The Cryosphere

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Abstract. We propose a waveform mixture algorithm to detect leads from CryoSat-2 data, which is novel and different from the existing threshold-based lead detection methods. The waveform mixture algorithm adopts the concept of spectral mixture analysis, which is widely used in the field of hyperspectral image analysis. This lead detection method was evaluated with high-resolution (250 m) MODIS images and showed comparable and promising performance in detecting leads when compared to the previous methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters (i.e., stack standard deviation, stack skewness, stack kurtosis, pulse peakiness, and backscatter σ0), as it directly uses L1B waveform data, unlike the existing threshold-based methods. Monthly lead fraction maps were produced by the waveform mixture algorithm, which shows interannual variability of recent sea ice cover during 2011–2016, excluding the summer season (i.e., June to September). We also compared the lead fraction maps to other lead fraction maps generated from previously published data sets, resulting in similar spatiotemporal patterns.

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Modified N-FINDR endmember extraction algorithm for remote-sensing imagery

Cited by 15 publications

References 28 publications

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

Collaborative Coupled Hyperspectral Unmixing Based Subpixel Change Detection for Analyzing Coastal Wetlands

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

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