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.
The demand for iron ore has increased in the recent years, thereby necessitating the adoption of rapid and accurate
approaches to iron ore exploration and its grade-assessment. It is in this context that hyperspectral radiometry is seen as a
potential tool. This paper examines the potential of hyperspectral radiometry in the visible, NIR and SWIR regions of the
EMR to assess the grades of hematite of the western Singhbhum iron ore belt of eastern India, in a rapid manner. Certain
spectro-radiometric measurements and geochemical analysis were carried out and the results have been presented. From
the spectral measurements, it is seen that the strength of reflectance and absorption at definite wavelength regions is
controlled by the chemical composition of the iron ores. It is observed that the primary spectral characteristics of these
hematite lie in the 650-750nm, 850 to 900nm and 2130-2230nm regions. The laboratory based hyperspectral signatures
and multiple regression analysis of spectral parameters and geochemical parameters (Fe2O3% and Al2O3%) predicted the
concentration of iron and alumina content in the hematite. A very strong correlation (R2=0.96) between the spectral
parameters and Fe% in the hematite with a minimum error of 0.1%, maximum error of 7.4% and average error of 2.6% is
observed. Similarly, a very strong correlation (R2=0.94) between the spectral parameters and Al2O3% in the iron ores
with a minimum error of 0.04%, maximum error of 7.49% and average error of 2.5% is observed. This error is perhaps
due to the presence of other components (SiO2, TiO2, P2O etc.) in the samples which can alter the degree of reflectance
and hence the spectral parameters. Neural network based multi-layer perception (MLP) analysis of various spectral
parameters and geochemical parameters helped to understand the relative importance of the spectral parameters for
predictive models. The strong correlations (Iron: R2=0.96; Alumina: R2=0.94) indicate that the laboratory hyperspectral
signatures in the visible, NIR and SWIR regions can give a better estimate of the grades of hematite in a rapid manner.
Mangrove ecosystem study is one of the main beneficiaries of the application of hyperspectral data and spectral
matching techniques. Diversity and density of mangrove species leads to complexity of the ecosystem. Hence, species
level mapping becomes difficult. Though hyperspectral images are appropriate for such a mapping, different mangrove
species with closely matching spectra pose a challenge. This paper proposes a novel hyperspectral matching algorithm
by integrating the stochastic Jeffries-Matusita measure (JM) and deterministic Spectral Angle Mapper (SAM) to
accurately map most species of the mangrove ecosystem. The JM-SAM algorithm signifies the combination of an
quantitative angle measure (SAM) and an qualitative distance measure (JM). The spectral capabilities of both the
measures are orthogonally projected using tangent and sine functions to result in the combined algorithm. The developed
JM-SAM algorithm is implemented to discriminate the mangrove species and the landcover classes of Pichavaram and
Muthupet mangrove forests of southern India using the Hyperion datasets. The developed algorithm is extended in a
supervised framework for improved classification of the Hyperion image. The reference spectra of the mangrove species
and other cover types are extracted from the Hyperion image. From the values of relative spectral discriminatory
probability and relative discriminatory entropy value, it can be inferred that hybrid JM-SAM matching measure results in
improved discriminability than the individual SAM and JM algorithms. This performance is reflected in the
classification results where the JM-SAM (TAN) and JM-SAM (SIN) matching algorithms yielded an improved accuracy
of (86.25%,85%) and (88.10%, 86.96) for both the study sites.
Image classification has evolved from per-pixel to sub-pixel and from sub-pixel to super resolution mapping approaches. Super-resolution mapping (SRM) is a technique which allows mapping at the sub-pixel scale. Super-resolution mapping proves to be the better approach for the accurate classification of coarse spatial resolution images and to resolve mixed pixels in the boundary of such images. The accuracy of the super-resolved output depends on the input derived from the soft classification technique. This paper aims to compare the potential of support vector machine (SVM), spectral angle mapper (SAM) and linear spectral unmixing (LSU) as inputs for super-resolution mapping. The fraction image, distance measure image and probability image obtained from linear spectral unmixing, spectral angle mapper and support vector machine respectively are used as an input for super resolution mapping designed on Hopfield Neural Network (HNN) for the Hyperion image of Peechi reservoir, south India. Effectiveness of the inputs is evaluated by estimating the water-spread area of the Peechi reservoir from each of the outputs. The results indicate that the accuracy of any super-resolution approach depends on the inputs from the soft classification approaches. The accuracy of the water spread area estimated from the classified outputs is 95.9%, 96.6% and 99.7% from LSU, SAM and SVM respectively as inputs for the SRM method. Thus, the HNN based SRM method proves to be better when the soft classification input is from SVM.
With more and more missions being launched to explore the Mars, the fact that water must have once flown it is no more
a mere speculation. Keeping this is mind, this paper attempts to interpret Martian and terrestrial images and provides an
insight into the conditions that must have prevailed on Mars when water flowed on it. This is achieved by comparing
regions selected on Mars that have evidences of a fluvial past, with regions of the Earth having similar geologic,
geomorphic and physiographic characteristics.
The Martian images and DEM were obtained from HiRISE onboard MRO of NASA. For the terrestrial regions, LandSat
8 (OLI) images and SRTM DEMs were used.
This study has brought out many similarities in the fluvial geomorphic regime of the two planets. The presence of lobate
structures, mouth bars and bifurcated channels in the Eberswalde Delta system on Mars is an indication of the interaction
of the fluvial system with a large standing body of water, similar to the Mississippi Delta system on Earth. Also, the
presence of braided pattern, streamlined bars and palaeochannels observed in the channels to the south of Ascraeus Mons
on Mars indicates a prominent flow of water through time, similar to the Yellowstone River system present on Earth.
This study thus aids in better understanding of the Martian fluvial processes and landforms.
Tropical mangrove forests along the coast evolve dynamically due to constant changes in the natural ecosystem and ecological cycle. Remote sensing has paved the way for periodic monitoring and conservation of such floristic resources, compared to labour intensive in-situ observations. With the laboratory quality image spectra obtained from hyperspectral image data, species level discrimination in habitats and ecosystems is attainable. One of the essential steps before classification of hyperspectral image data is band selection. It is important to eliminate the redundant bands to mitigate the problems of Hughes effect that are likely to affect further image analysis and classification accuracy. This paper presents a methodology for the selection of appropriate hyperspectral bands from the EO-1 Hyperion image for the identification and mapping of mangrove species and coastal landcover types in the Bhitarkanika coastal forest region, eastern India. Band selection procedure follows class based elimination procedure and the separability of the classes are tested in the band selection process. Individual bands are de-correlated and redundant bands are removed from the bandwise correlation matrix. The percent contribution of class variance in each band is analysed from the factors of PCA component ranking. Spectral bands are selected from the wavelength groups and statistically tested. Further, the band selection procedure is compared with similar techniques (Band Index and Mutual information) for validation. The number of bands in the Hyperion image was reduced from 196 to 88 by the Factor-based ranking approach. Classification was performed by Support Vector Machine approach. It is observed that the proposed Factor-based ranking approach performed well in discriminating the mangrove species and other landcover units compared to the other statistical approaches. The predominant mangrove species Heritiera fomes, Excoecaria agallocha and Cynometra ramiflora are spectral identified and the health status of these species are assessed by the selected band. Further, the performance of this band selection approaches are evaluated in multi-sensor image fusion for better mapping of mangrove ecosystems, wherein spatial resolution is enhanced while retaining the optimal number of hyperspectral bands.
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 approach of spectral unmixing of ASTER image data delineated areas rich in alumina.
Various geological and geomorphological parameters that control bauxite formation were also derived from the ASTER
image. All these information, when integrated, showed that there are 16 cappings (including the existing mines) that
satisfy most of the conditions favouring bauxitization in the Kolli Hills. The study concludes that spectral unmixing of
hyperspectral satellite data in the VNIR and SWIR regions may be combined with the terrain parameters to get accurate
information about bauxite deposits, including their quality.
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