sanjeevi s - Academia.edu (original) (raw)

Papers by sanjeevi s

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 wellbuilt 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.

Though the estimation of the water-spread area in reservoirs is often carried out by field survey... more 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 waterspread 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.

This paper proposes a novel hyperspectral matching technique by integrating the Jeffries-Matusita... more 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.