This study attempts grade-wise mapping in a limestone mine in Ariyalur, Southern India, and in th... more This study attempts grade-wise mapping in a limestone mine in Ariyalur, Southern India, and in the iron ore mines of Noamundi, Eastern India. After noise removal in the Sentinel 2A (multispectral) and EO-1 Hyperion (hyperspectral) image datasets, spectral matching is performed using the Jeffries-Matusita (JM) distance, Spectral Correlation Mapper (SCM), and combined JM-SCM measure. Due to the specific absorption spectra for carbonates (1900 nm, 2000 nm, and 2160 nm) and iron oxide (865 nm), it is possible to identify and map such mineral deposits using the multispectral dataset (Sentinel 2A) and hyperspectral dataset (EO-1 Hyperion) respectively. The grade-wise mapping of carbonate in the Ariyalur mine using the Sentinel 2A dataset by the Jeffries-Matusita (JM) approach and Spectral Correlation Mapper (SCM) yielded R2 values of 0.44 and 0.77 respectively, whereas the combined JM-SCM approach resulted in a higher correlation with an R2 value of 0.87. The grade-wise mapping of iron oxide in Noamundi using the EO-1 Hyperion dataset by the Jeffries-Matusita (JM) approach and the Spectral Correlation Mapper (SCM) approach yielded R2 values of 0.15 and 0.76, respectively, whereas the combined JM-SCM approach resulted in a higher correlation with an R2 value of 0.90. Such an improved performance of the combined approach is primarily due to the simultaneous and effective utilization of band-wise information (by JM) and correlation aspects (by SCM) of the reference and target spectra considered in the matching algorithm. Thus, in this study, the proposed algorithm proved its compatibility and utility in extracting information on mineral abundance distribution for mine areas.
Many spectral matching algorithms, ranging from the traditional clustering techniques to the rece... more Many spectral matching algorithms, ranging from the traditional clustering techniques to the recent automated matching models, have evolved. This paper provides a review and up-to-date information on the past and current role of the spectral matching approaches adopted in hyperspectral satellite image processing. The need for spectral matching has been deliberated and a list of spectral matching algorithms has been compared and described. A review of the conventional spectral angle measures and the advanced automated spectral matching tools indicates that, for better performance of target detection, there is a need for combining two or more spectral matching techniques. From the studies of several authors, it is inferred that continuous improvement in the matching techniques over the past few years is due to the need to handle and analyse hyperspectral image data for various applications. The need to develop a well-built and specialized spectral library to accommodate the resources from enormous spectral data is suggested. This may improve accuracy in mineral and soil mapping, vegetation species identification and health monitoring, and target detection. The future role of cloud computing in accessing globally distributed spectral libraries and performing spectral matching is highlighted. Rather than inferring that a particular matching algorithm is the best, this paper points out the requirements of an ideal algorithm. With increasing usage of hyperspectral data for resources mapping, the review presented in this paper will certainly benefit the large and emerging community of hyperspectral image users.
Abstract Though the estimation of the water-spread area in reservoirs is often carried out by fie... more Abstract Though the estimation of the water-spread area in reservoirs is often carried out by field surveys, it is time-consuming and tedious, and cannot be done periodically. To overcome this issue, satellite images are often used where the estimation is made through density slicing or conventional per-pixel classification. This results in an inaccurate estimation of reservoir capacity. The high cost and nonavailability of high-resolution images demands the use of an alternative approach that can give accurate information about the reservoir water-spread area. A hyperspectral image (Hyperion) of moderate resolution is used for the accurate estimation of the water-spread area of Peechi reservoir, southern India. The reservoir water-spread area obtained from per-pixel classification, subpixel classification, and super-resolution mapping approaches are compared with the water-spread area obtained from the ground truth hydrographic survey data. It is observed that the water-spread area estimated from the hyperspectral image by the per-pixel approach is 7.66 sq km , that by the subpixel approach is 6.34 sq km , and that by the super-resolution approach is 5.69 sq km compared to the actual area of 5.95 sq km . The classification accuracy estimated for the Hopfield neural network based super-resolution technique is 92.97%, whereas that for the conventional classifier (maximum likelihood) is 86.72%. This improved accuracy in classification resulted in an accurate estimation of water-spread area. Hence, it is inferred that super-resolution mapping applied to hyperspectral images is a computationally efficient approach for the accurate quantification of reservoir water-spread area.
International Journal of Applied Earth Observation and Geoinformation, 2014
Abstract This paper proposes a novel hyperspectral matching technique by integrating the Jeffries... more Abstract This paper proposes a novel hyperspectral matching technique by integrating the Jeffries-Matusita measure (JM) and the Spectral Angle Mapper (SAM) algorithm. The deterministic Spectral Angle Mapper and stochastic Jeffries-Matusita measure are orthogonally projected using the sine and tangent functions to increase their spectral ability. The developed JM-SAM algorithm is implemented in effectively discriminating the landcover classes and cover types in the hyperspectral images acquired by PROBA/CHRIS and EO-1 Hyperion sensors. The reference spectra for different land-cover classes were derived from each of these images. The performance of the proposed measure is compared with the performance of the individual SAM and JM approaches. From the values of the relative spectral discriminatory probability (RSDPB) and relative discriminatory entropy value (RSDE), it is inferred that the hybrid JM-SAM approach results in a high spectral discriminability than the SAM and JM measures. Besides, the use of the improved JM-SAM algorithm for supervised classification of the images results in 92.9% and 91.47% accuracy compared to 73.13%, 79.41%, and 85.69% of minimum-distance, SAM and JM measures. It is also inferred that the increased spectral discriminability of JM-SAM measure is contributed by the JM distance. Further, it is seen that the proposed JM-SAM measure is compatible with varying spectral resolutions of PROBA/CHRIS (62 bands) and Hyperion (242 bands).
This paper makes an effort to compare the recently evolved soft classification method based on Li... more This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood ...
Journal of the Indian Society of Remote …, Jan 1, 2011
... 152. Helmi Zulhaidi Mohd Shafri, Mohamad Amran Mohd Salleh, & Azadesh Gh... more ... 152. Helmi Zulhaidi Mohd Shafri, Mohamad Amran Mohd Salleh, & Azadesh Ghiyamat (2006) Hyperspectral remotesensing of vegetation using red edge position techniques. American Journal of Applied Sciences 3(6): 18641871 Milton, EJ (1987). ...
Abstract This paper present the results of a preliminary study to assess the potential of the vis... more Abstract This paper present the results of a preliminary study to assess the potential of the visible, NIR and SWIR energy of the EMR in differentiating iron ores of different grades in a rapid manner using hyperspectral radiometry. Using different iron ore samples from ...
This paper presents a study about the potential of remote sensing in bauxite exploration in the K... more This paper presents a study about the potential of remote sensing in bauxite exploration in the Kolli hills of Tamilnadu state, southern India. ASTER image (acquired in the VNIR and SWIR regions) has been used in conjunction with SRTM-DEM in this study. A new ...
Traditional approaches of image classification, such as maximum likelihood and the band threshold... more Traditional approaches of image classification, such as maximum likelihood and the band thresholding method, involve the per-pixel approach to delineate the water spread area of a reservoir. One of the limitations of these approaches is that the pixels representing the ...
Abstract Linear spectral unmixing of airborne multispectral scanner (CASI= Compact Airborne Spect... more Abstract Linear spectral unmixing of airborne multispectral scanner (CASI= Compact Airborne Spectrographic Imager) imagery is performed using the constrained least squares method. The subpixel proportions of sand, vegetation and shadow/moisture are defined to ...
This study attempts grade-wise mapping in a limestone mine in Ariyalur, Southern India, and in th... more This study attempts grade-wise mapping in a limestone mine in Ariyalur, Southern India, and in the iron ore mines of Noamundi, Eastern India. After noise removal in the Sentinel 2A (multispectral) and EO-1 Hyperion (hyperspectral) image datasets, spectral matching is performed using the Jeffries-Matusita (JM) distance, Spectral Correlation Mapper (SCM), and combined JM-SCM measure. Due to the specific absorption spectra for carbonates (1900 nm, 2000 nm, and 2160 nm) and iron oxide (865 nm), it is possible to identify and map such mineral deposits using the multispectral dataset (Sentinel 2A) and hyperspectral dataset (EO-1 Hyperion) respectively. The grade-wise mapping of carbonate in the Ariyalur mine using the Sentinel 2A dataset by the Jeffries-Matusita (JM) approach and Spectral Correlation Mapper (SCM) yielded R2 values of 0.44 and 0.77 respectively, whereas the combined JM-SCM approach resulted in a higher correlation with an R2 value of 0.87. The grade-wise mapping of iron oxide in Noamundi using the EO-1 Hyperion dataset by the Jeffries-Matusita (JM) approach and the Spectral Correlation Mapper (SCM) approach yielded R2 values of 0.15 and 0.76, respectively, whereas the combined JM-SCM approach resulted in a higher correlation with an R2 value of 0.90. Such an improved performance of the combined approach is primarily due to the simultaneous and effective utilization of band-wise information (by JM) and correlation aspects (by SCM) of the reference and target spectra considered in the matching algorithm. Thus, in this study, the proposed algorithm proved its compatibility and utility in extracting information on mineral abundance distribution for mine areas.
Many spectral matching algorithms, ranging from the traditional clustering techniques to the rece... more Many spectral matching algorithms, ranging from the traditional clustering techniques to the recent automated matching models, have evolved. This paper provides a review and up-to-date information on the past and current role of the spectral matching approaches adopted in hyperspectral satellite image processing. The need for spectral matching has been deliberated and a list of spectral matching algorithms has been compared and described. A review of the conventional spectral angle measures and the advanced automated spectral matching tools indicates that, for better performance of target detection, there is a need for combining two or more spectral matching techniques. From the studies of several authors, it is inferred that continuous improvement in the matching techniques over the past few years is due to the need to handle and analyse hyperspectral image data for various applications. The need to develop a well-built and specialized spectral library to accommodate the resources from enormous spectral data is suggested. This may improve accuracy in mineral and soil mapping, vegetation species identification and health monitoring, and target detection. The future role of cloud computing in accessing globally distributed spectral libraries and performing spectral matching is highlighted. Rather than inferring that a particular matching algorithm is the best, this paper points out the requirements of an ideal algorithm. With increasing usage of hyperspectral data for resources mapping, the review presented in this paper will certainly benefit the large and emerging community of hyperspectral image users.
Abstract Though the estimation of the water-spread area in reservoirs is often carried out by fie... more Abstract Though the estimation of the water-spread area in reservoirs is often carried out by field surveys, it is time-consuming and tedious, and cannot be done periodically. To overcome this issue, satellite images are often used where the estimation is made through density slicing or conventional per-pixel classification. This results in an inaccurate estimation of reservoir capacity. The high cost and nonavailability of high-resolution images demands the use of an alternative approach that can give accurate information about the reservoir water-spread area. A hyperspectral image (Hyperion) of moderate resolution is used for the accurate estimation of the water-spread area of Peechi reservoir, southern India. The reservoir water-spread area obtained from per-pixel classification, subpixel classification, and super-resolution mapping approaches are compared with the water-spread area obtained from the ground truth hydrographic survey data. It is observed that the water-spread area estimated from the hyperspectral image by the per-pixel approach is 7.66 sq km , that by the subpixel approach is 6.34 sq km , and that by the super-resolution approach is 5.69 sq km compared to the actual area of 5.95 sq km . The classification accuracy estimated for the Hopfield neural network based super-resolution technique is 92.97%, whereas that for the conventional classifier (maximum likelihood) is 86.72%. This improved accuracy in classification resulted in an accurate estimation of water-spread area. Hence, it is inferred that super-resolution mapping applied to hyperspectral images is a computationally efficient approach for the accurate quantification of reservoir water-spread area.
International Journal of Applied Earth Observation and Geoinformation, 2014
Abstract This paper proposes a novel hyperspectral matching technique by integrating the Jeffries... more Abstract This paper proposes a novel hyperspectral matching technique by integrating the Jeffries-Matusita measure (JM) and the Spectral Angle Mapper (SAM) algorithm. The deterministic Spectral Angle Mapper and stochastic Jeffries-Matusita measure are orthogonally projected using the sine and tangent functions to increase their spectral ability. The developed JM-SAM algorithm is implemented in effectively discriminating the landcover classes and cover types in the hyperspectral images acquired by PROBA/CHRIS and EO-1 Hyperion sensors. The reference spectra for different land-cover classes were derived from each of these images. The performance of the proposed measure is compared with the performance of the individual SAM and JM approaches. From the values of the relative spectral discriminatory probability (RSDPB) and relative discriminatory entropy value (RSDE), it is inferred that the hybrid JM-SAM approach results in a high spectral discriminability than the SAM and JM measures. Besides, the use of the improved JM-SAM algorithm for supervised classification of the images results in 92.9% and 91.47% accuracy compared to 73.13%, 79.41%, and 85.69% of minimum-distance, SAM and JM measures. It is also inferred that the increased spectral discriminability of JM-SAM measure is contributed by the JM distance. Further, it is seen that the proposed JM-SAM measure is compatible with varying spectral resolutions of PROBA/CHRIS (62 bands) and Hyperion (242 bands).
This paper makes an effort to compare the recently evolved soft classification method based on Li... more This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood ...
Journal of the Indian Society of Remote …, Jan 1, 2011
... 152. Helmi Zulhaidi Mohd Shafri, Mohamad Amran Mohd Salleh, & Azadesh Gh... more ... 152. Helmi Zulhaidi Mohd Shafri, Mohamad Amran Mohd Salleh, & Azadesh Ghiyamat (2006) Hyperspectral remotesensing of vegetation using red edge position techniques. American Journal of Applied Sciences 3(6): 18641871 Milton, EJ (1987). ...
Abstract This paper present the results of a preliminary study to assess the potential of the vis... more Abstract This paper present the results of a preliminary study to assess the potential of the visible, NIR and SWIR energy of the EMR in differentiating iron ores of different grades in a rapid manner using hyperspectral radiometry. Using different iron ore samples from ...
This paper presents a study about the potential of remote sensing in bauxite exploration in the K... more This paper presents a study about the potential of remote sensing in bauxite exploration in the Kolli hills of Tamilnadu state, southern India. ASTER image (acquired in the VNIR and SWIR regions) has been used in conjunction with SRTM-DEM in this study. A new ...
Traditional approaches of image classification, such as maximum likelihood and the band threshold... more Traditional approaches of image classification, such as maximum likelihood and the band thresholding method, involve the per-pixel approach to delineate the water spread area of a reservoir. One of the limitations of these approaches is that the pixels representing the ...
Abstract Linear spectral unmixing of airborne multispectral scanner (CASI= Compact Airborne Spect... more Abstract Linear spectral unmixing of airborne multispectral scanner (CASI= Compact Airborne Spectrographic Imager) imagery is performed using the constrained least squares method. The subpixel proportions of sand, vegetation and shadow/moisture are defined to ...
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