Weighted spectral correlation angle target detection method for land-based hyperspectral imaging

Abstract

In land-based spectral imaging, the spectra of ground objects are inevitably affected by the imaging conditions (weather conditions, atmospheric conditions, light conditions, zenith and azimuth angle conditions) and spatial distribution of targets, leading to uncertainties featured by “same object different spectrum”. That is, the spectrum of a ground object may change within a certain range under different imaging conditions. Traditional target detection (TD) methods are mainly based on similarity measurements and do not fully account for the spectral uncertainties. These detection methods are prone to false detections or missed detections. Therefore, reducing the impact of spectral uncertainties on TD is an important research topic in hyperspectral imaging. In this paper, we first review traditional TD methods and compare their principles and characteristics. It is found that the spectral correlation angle (SCA) method has good adaptability in land-based imaging. The shortcoming of the SCA method that it cannot reflect the local spectrum characteristics, is also analyzed. As the effect of spectral uncertainties cannot be completely overcome by the SCA method, a new similarity measurement method, the weighted spectral correlation angle (WSCA) method, is proposed. It can reduce the influence of spectral uncertainties on TD by increasing the weight of particular bands. Finally, we use two sets of experiments to analyze the effect of the WSCA method on TD. Its performance in overcoming spectral uncertainties caused by variations in imaging conditions or uneven spatial distributions of targets is tested. The results show that the WSCA method can effectively reduce the influence of spectral uncertainties and obtain a good detection result.

Graphical abstract

Keywords

Hyperspectral image / Spectral uncertain feature / Target detection / Land-based imaging condition / Weighted spectral correlation angle (WSCA)

References

[1] Chang, C. , Chiang, S. : Anomaly detection and classification for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 40 (6), 1314- 1325 (2002)

[2] Bajorski, P. : Target detection under misspecified models in hyperspectral images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5 (2), 470- 477 (2012)

[3] Sedaghat, A. , Mokhtarzade, M. , Ebadi, H. : Uniform robust scaleinvariant feature matching for optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 49 (11), 4516- 4527 (2011)

[4] Qian, S. : Hyperspectral satellites, evolution, and development history. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 7032- 7056 (2021)

[5] Iordanche, M. , Dias, J. , Plaza, A. : Sparse unmixing of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 49 (6), 2014- 2039 (2011)

[6] Guo, T. , Luo, F. , Zhang, L. , Zhang, B. , Tan, X. , Zhou, X. : Learning structurally incoherent background and target dictionaries for hyperspectral target detection. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 3521- 3533 (2020)

[7] Yang, L. , Peng, J. , Su, H. , Xu, L. , Wang, Y. , Yu, B. : Combined nonlocal spatial information and spatial group sparsity in NMF for hyperspectral unmixing. IEEE Geosci. Remote Sens. Lett. 17 (10), 1767- 1771 (2020)

[8] Tan, X. , Xue, Z. : Spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification. Eur. J. Remote Sens. 55 (1), 409- 419 (2022)

[9] Foy, B.R. , Theiler, J. , Fraser, A.M. : Decision boundaries in two dimensions for target detection in hyperspectral imagery. Opt. Express 17 (20), 17391- 17411 (2009)

[10] Matteoli, S. , Diani, M. , Corsini, G. : Improved estimation of local background covariance matrix for anomaly detection in hyperspectral images. Opt. Eng. 49 (4), 258 (2010)

[11] Settle, J. : On constrained energy minimization and the partial unmixing of multispectral images. IEEE Trans. Geosci. Remote Sens. 40 (3), 718- 721 (2002)

[12] Chang, C. , Heinz, D. : Constrained subpixel target detection for remotely sensed imagery. IEEE Trans. Geosci. Remote Sens. 38 (3), 1144- 1159 (2000)

[13] Camps-Valls, G. : Kernel spectral angle mapper. Electron. Lett. 52 (14), 1218- 1220 (2016)

[14] Su, H. , Du, P. , Du, Q. : Semi-supervised dimensionality reduction using orthogonal projection divergence-based clustering for hyperspectral imagery. Opt. Eng. 51 (11), 111715 (2012)

[15] Robila, S.A. : Using spectral distances for speedup in hyperspectral image processing. Int. J. Remote Sens. 26 (24), 5629- 5650 (2005)

[16] Zhong, Y. , Lin, X. , Zhang, L. : A support vector conditional random fields classifier with a Mahalanobis distance boundary constraint for high spatial resolution remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7 (4), 1314- 1330 (2014)

[17] Sieh, M. , Tsai, S. : Decoding frequency permutation arrays under Chebyshev distance. IEEE Trans. Inf. Theory 56 (11), 5730- 5737 (2010)

[18] Zha, Y. , Qiu, Z. , Zhang, P. , Huang, W. : Unsupervised ensemble hashing: boosting minimum hamming distance. IEEE Access 8, 42937- 42947 (2020)

[19] Vander Meero, F. , Bakker, W. : Cross correlogram spectral matching: application to surface mineralogical mapping by using AVIRIS data from Cuprite, Nevada. Remote Sens. Environ. 61 (3), 371- 382 (1997)

[20] Kerekes, J. : Receiver operating characteristic curve confidence intervals and regions. IEEE Geosci. Remote Sens. Lett. 5 (2), 251- 255 (2008)

[21] Chang, C. : Multiparameter receiver operating characteristic analysis for signal detection and classification. IEEE Sens. J. 10 (3), 423- 442 (2010)

RIGHTS & PERMISSIONS

2023 The Author(s)