International Journal of Applied Earth Observation and Geoinformation
Using spectrum differentiation and combination for target detection of minerals
Introduction
Imaging Spectrometry takes the advantage of contiguous spectral channels to unveil signal sources that usually cannot be resolved by multispectral sensors (Chang, 2003, Chang, 2007, Clark, 1999). In hyperspectral data exploitation, most targets of interest are those with small spatial presence and low probability existence in either form of mixed pixel or sub pixel (Chang, 2007, Chang and Heinz, 2000, Schowengerdt, 2007). Therefore, literal based image processing techniques (Chang, 2007, Van der Meer, 2006, Van der Meer et al., 2012, Zhang and Pazner, 2007) are not effective in hyperspectral images even if they can be applied. Derivative spectroscopy and spectral analysis, on the other hand, offers a valuable alternative in this area.
Numerical differentiation is a common tool used in analytical chemistry for processing one-dimensional signals since a derivative is sensitive to important yet subtle details in a spectrum. Hyperspectral data nature allows the implementation of such techniques in this kind of image data (DemetridesShah et al., 1990). Application of this technique in remote sensing is due to its ability for resolving complex spectra of several target species within individual pixels (DemetridesShah et al., 1990), and the fact that derivatives of second or higher order are insensitive to illumination variations caused by sun angle, cloud cover, or topography (Tsai and Philpot, 1998, Tsai and Philpot, 2002). Additionally, it is worth considering that the spectral shape information is one of the most important measures in evaluation of the spectral similarity between two spectra (Clark et al., 2003, Hungate et al., 2008, Van der Meer and De Jong, 2011). As it was mentioned in (Dehnavi et al.,, 2014, Tsai and Philpot, 1998, Zhang et al., 2010 Tsai and Philpot, 1998; Zhang et al., 2010), derivatives of different orders contain various levels of spectral shape information in a spectrum. Therefore, it is not unexpected to have better detection on signal sources in an individual pixel by the usage of derivative spectrum and consequently its spectral shape information. Moreover, spectrum matching techniques are sometimes ruined, in as much as the library spectrum is less fitted to the field spectrum in contrast to its derivative (Kim, 2011). Taken together, it is of great value applying information content of derivative spectrum in target detection.
Several investigators hinted that employing derivative signature has improved the results of their research in different application areas, including but not limited to the following: Torrecilla et al. took the advantage of derivative spectrum for identification of water constituents in oceanographic regions (Torrecilla et al., 2009). Algal chlorophyll concentration was estimated in (Han, 2005) by application of derivative analysis. The first derivatives were computed and correlated with the chlorophyll-a concentration in a shallow water in (Jensen, 2009). Second derivative approximation was applied in (Becker et al., 2005) in order to help identifying spectral regions where the spectral bands are the most botanically explanative. Partial abundances of spectrally similar minerals in complex mixtures were estimated in (Debba, 2009, Debba et al., 2006), applying first and second orders of derivative spectra. Toxic algal bloom was detected in (Craig et al., 2006) from the analysis of the fourth derivative of phytoplankton absorption spectra, estimated from in situ hyperspectral measurements of reflectance. First and second orders of derivative spectrum were also used in previous researches for un-mixing purposes (see (Bieniarz et al., 2012, Hengqiana et al., 2012, Zhang et al., 2004)). In an earlier research, second order derivative spectrum was applied in linear spectral mixture model (LSM) aimed to estimate minerals’ abundance (Zhang et al., 2004). Results of their research suggested an increase in the accuracy of the abundance estimates. However as is clear, the usage of higher order derivatives is neglected in these researches, specifically in target detection.
The spectrum differentiation concept was also previously employed in binary encoding (Kim, 2011), spectral derivative based feature coding (SDFC) concepts (Chang, 2013, Chang et al., 2009), and also the determination of absorption features by using gradient changes in the spectral reflectance curve (Rezaei et al., 2012) for endmember extraction purposes. However, in such coding methods the information content of whole derivative signature was not applied.
In sum, previous researches have proven the practical usefulness of derivative spectrum in different fields of research. However, many of the earlier works were carried at the laboratory level (see for example (Dehnavi et al., 2015), whereby some orders of derivative spectrum were introduced as good discriminators for mineral targets). On the other hand, they rarely or never discussed application of higher order derivative spectrum, i.e. 3rd to 5th orders, in target detection of hyperspectral images. The first idea of this work is thus, putting in various orders of derivative spectrum (1st to 5th orders) into CEM algorithm, introduced as “Derivative Constrained Energy Minimization” or “DCEM”, and exploring the detection maps in available image data.
Moreover, one of the points that we have to pay special attention is that spectrum differentiation eliminates low frequency components of the spectrum. Despite the little information included in the low frequency components of a signal or spectrum, their complete elimination cause an information loss problem. Having a spectra and its best derivative order (as a result of DCEM) in a unified approach cause an increase in the information we could obtain from a spectral curve. Hence, in the second step of this research an ensemble classifier approach (Dietterich, 2000, Dietterich, 2002, Rokach, 2010, Zhang et al., 2010) was applied for the combined use of both spectra and the best derivative order. This combined uses of derivative and zero order spectra is introduced as “Ensemble Constrained Energy Minimization” or “ECEM”. Thereupon, it can be claimed that the majority of the information contained in a target's spectral curve is used in ECEM.
To justify the above-mentioned frameworks, this study was carried out with an airborne hyperspectral data in comparison to the previous works whereby the standard laboratory images were applied (Zhang et al., 2004). Both proposed detection algorithms were used for identification of four mineral targets including alunite, kaolinite, epidote and hematite which were located in a hydrothermally altered mineral region in Iran east. Due to the possible economic importance of various minerals within the study area better mapping performance was required. To this end, our proposed method was used for better mapping of the region.
A description on the methods applied in this work and the evaluation methodology is presented in Section 2. Section 3 focuses on the study area, experimental and ground truth data; laboratory measurements, pre-processing step and experimental results are discussed in Sections 4–6 respectively. Finally, we draw out our conclusion in Section 7.
Section snippets
Method
This section provides a full description of the proposed three-step approach for target detection. First, derivative spectrum was used for target detection in the proposed DCEM approach. Second, one of the derivative orders was selected as the best order for the identification of each target. Third, a combination of both spectra and its derivative spectrum was applied in the proposed ECEM approach.
Experimental data
Study was conducted via a HyMap airborne hyperspectral dataset (Bannon, 2009, Cocks et al., 1998; Rica, 2005; Schapfer, 2006) in Gonabad county, Khorasan Razavi Province, Iran east (Lat. 34°25'N and Lon. 58°34'E). The HyMap images were acquired on September 11th 2006 with a spatial resolution of 5 × 5 m. HyMap sensor has been developed in Australia by Integrated Spectronics, HyVista Corporation and now deployed for commercial operations around the world. The specific aspects of the sensor is
Laboratory measurements
Laboratory measurements are required not only for the detection of the mineral targets, but also for the validation of the results. Thus, in what follows three different lab measurements required in this research are presented and explained in detail.
After the completion of the field sampling procedure, samples were transferred to the laboratory. They were firstly prepared by some expert geologists prior to the spectral measurements. For this purpose, all samples were first round to rock powder
Pre-processing
Pre-processing phase is a basic and important step toward the success of target detectors especially when derivative spectrum is used. The data products delivered by HyVista Corporation were radiance calibrated (at sensor), and geometric correction look up tables. All data were delivered as ENVI compatible files. Since the spectral and radiometric calibration of the HyMap sensor were accomplished prior to the survey, this information helped convert the raw DN counts to radiance values in
Results and discussion
Detection maps were generated based on the proposed DCEM and ECEM approaches. Then, a model-based geometric correction approach was used for geo-correction and geo-referencing of the detection maps. For this, a geometric lookup table (.GLT) file prepared by HyVista Corporation based on HyMap sensoring model was used. Georeferenced detection maps for the targets of interest are presented in Fig. 10, Fig. 11, Fig. 12, Fig. 13 . Fig. 10, Fig. 11, Fig. 12, Fig. 13(a)–(c) show the detection results
Conclusion
The primary purpose of this research was to evaluate the potential of derivative spectrum in target detection. For this, derivative spectra was in CEM target detection algorithm and a new approach introduced as “DCEM”.
This study was carried out with an airborne hyperspectral data in comparison to the previous works whereby the standard laboratory images were applied. Study was conducted via an airborne hyperspectral HyMap data in a hydrothermally altered mineral region in Iran East. This region
References (61)
- et al.
Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis
Remote Sens. Environ.
(2005) - et al.
Spectral derivative feature coding for hyperspectral signature analysis
Pattern Recogn.
(2009) - et al.
Combining multiple classifiers using vote based classifier ensemble technique for named entity recognition
Data Knowledge Eng.
(2013) - et al.
Multi-and hyperspectral geologic remote sensing: a review
Int. J. Appl. Earth Obs. Geoinf.
(2012) - et al.
FLAASH, a MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations
Proc. 7th Ann. JPL Airborne Earth Science Workshop
(1998) Hyperspectral imaging: cubes and slices
Nat. Photonics
(2009)- et al.
Sparse approximation, coherence and use of derivatives in hyperspectral unmixing
4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing WHISPERS
(2012) - et al.
Hyperspectral subspace identification
IEEE Trans. Geosci. Remote Sens.
(2008) - et al.
Constrained subpixel terget detection for remotely sensed imagery
IEEE Trans. Geosci. Remote Sens.
(2000) Hyperspectral Imaging: Techniques for Spectral Detection and Classification
(2003)
Hyperspectral Data Exploitation: Theory and Applications
Hyperspectral Data Processing: Algorithm Design and Analysis
Imaging spectroscopy: earth and planetary remote sensing with the USGS Tetracorder and expert systems
J. Geophys. Res.
Abundance estimation of spectrally similar minerals by using derivative spectra in simulated annealing
IEEE Trans. Geosci. Remote Sens.
Abundance estimation of spectrally similar minerals
High-order derivative spectrum in hydrothermally altered minerals discrimination
J. Appl. Remote Sens.
High resolution derivative spectra in remote sensing
Remote Sens. Environ.
Pairwise classification as an ensemble technique
European Conference on Machine Learning
General least-squares smoothing and differentiation by the convolotion (Savitzky-Golay) method
Anal. Chem.
Estimating chlorophyll-a concentration using first-derivative spectra in coastal water
Int. J. Remote Sens.
Detection of subpixel spectral signatures in hyperspectral image sequences
Annual Meeting Proceedings of American Society of Photogrammetry & Remote Sensing
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