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This article presents an efficient novel approach estimating residual attitude based on geometrically corrected (GEO) satellite images. A technique is presented that uses orbital plane geometry to compute the rotation angle as a function of geographic latitude between GEO image space and radiometrically corrected (RAD) image space. First, a nonlinear forward model is established that translates the residual errors in roll, pitch, and yaw to scan errors and pixel errors in GEO image space. Subsequently, the inverse problem is solved using Newton's method of nonlinear optimization for estimating residual roll, pitch, and yaw. We demonstrate our results on data products of the high-resolution Indian satellites Cartosat-2E and Cartosat-2F. Further, the superiority of the proposed method is established by comparing it with multiple existing methods in the literature. The R2 measures of goodness of fit for roll, pitch, and yaw estimation based on RAD and GEO products using the proposed method are 0.65, 0.99, and 0.65, respectively; using the existing method, they are 0.074, 0.005, and 0.50. [ABSTRACT FROM AUTHOR]

The role of feature-level fusion using the Dempster-Shafer rule and that of the data-level fusion in the Markov random field (MRF) context is studied to obtain an optimally segmented image. Subsequently, segments are validated, and classification accuracy for the test data is evaluated.

An unsupervised segmentation approach to classification of multispectral image is suggested here in Markov random field (MRF) frame work. This work generalizes the work of Sarkar et al. [32] on gray value images for multispectral images and is extended for landuse classification. The essence of this approach is based on capturing intrinsic characters of tonal and textural regions of any multispectral image. The approach takes an initially oversegmented image and the original multispectral image as the input and defines a MRF over region adjacency graph (RAG) of the initially segmented regions. Energy function minimization associated with the MRF is carried out by applying a multivariate statistical test. A cluster validation scheme is outlined after obtaining optimal segmentation. Quantitative evaluation of classification accuracy of test data for three illustrations are shown and compared with conventional maximum likelihood procedure. Comparison of the proposed methodology with a recent work of texture segmentation in the literature has also been provided. The findings of the proposed method are found to be encouraging. Index Terms--Cluster validation, Hotelling's [T.sup.2] statistic, initial segmentation, Markov random field (MRF), QRcp, SVD.

The analysis of remotely sensed imagery for various applications have evolved over the past three decades from manual (visual) interpretation to computer-based approaches. Sophisticated digital techniques have been developed to tackle specific image processing tasks in the analysis, and the need for efficient reference to collateral data like maps have led to special handling systems called Geographic Information Systems. However, the automation of the aspect of human expert's interaction is still in its adolescence, and only recently has there been some interest in finding methods or models for representing the expert's knowledge and inference mechanism so as to build expert systems for remote sensing image analysis. In this paper a framework to represent a broad class of problems in the analysis of remote sensing imagery is proposed, and an inference engine to tackle such problems is derived. A simple model for spectral knowledge representation is used along with a method for quantification of knowledge through an evidential approach. An automatic knowledge extraction technique is also proposed to gather knowledge from training samples. The techniques of knowledge extraction, representation and inferencing have been used to do a land cover analysis on two data sets, and the results are compared with contemporary digital techniques. It is found that the proposed approach has the advantages of avoiding commission errors, and can incorporate non-spectral and collateral knowledge, while its accuracy using only spectral knowledge is comparable with standard digital methods.

With the launch of the Indian remote sensing satellite Cartosat-1, an along-track stereoscopic imaging mission of the Indian Space Research Organisation (ISRO), new possibilities for operational availability of high-resolution stereo-imagery from space for the remote sensing and cartography user communities have emerged. The high-resolution stereo data beamed from twin cameras on board the Cartosat-1 mission facilitates topographic mapping up to 1:25 000 scale. The primary advantage of the Cartosat-1 mission is seen in the generation of digital elevation models (DEMs) and the production of ortho-images in an operational set-up. This also facilitates 3D terrain visualisation for very large tracts of land. Stereo Strip Triangulation (SST) is a software system developed and perfected at the Space Applications Centre of ISRO for operational generation of secondary control and appropriate DEMs for subsequent use in the generation of ortho-images. This system has been in use for almost 2 years at the National Remote Sensing Agency in Hyderabad, India, and has generated a wealth of data for use in topographic mapping. An initiative to generate a database of seamless, homogeneous DEMs and associated ortho-image tiles at country level has been undertaken by ISRO. This data-set has been named CartoDEM. The Cartosat-1 data processing team has completed the design and testing of software for the generation of the CartoDEM. This software system has undergone detailed evaluation and currently is in the final stage of development of the operational procedures required to make maximum use of the capabilities of the Cartosat-1 sensors. A data dissemination software system is currently under development. As part of the large-scale evaluation exercises to finalise the specifications of CartoDEM, it is established that with the 2·5 m ground resolution, a base-to-height ratio of 0·62 and with capability to register conjugate points in the stereopair to sub-pixel level, DEMs can be generated at 0·3 arc second intervals, with a height accuracy of 3 to 4 m, over tracts of undulating land mass up to 15 000 km2 with the use of 10 to 20 ground control points. The Cartosat-1 data processing and evaluation team regularly monitors the radiometric quality of images. As part of the radiometric characterisation of sensors, the team computed point spread functions (PSFs) for the two cameras of Cartosat-1. Special filters based on the PSFs then work to improve the radiometric quality of the images. Initial results from these exercises show good promise in image restoration based on PSFs for Cartosat-1. This paper presents a summary of activities and exercises related to (i) Stereo Strip Triangulation, (ii) CartoDEM, (iii) image quality improvement using the PSF-based image restoration and (iv) block adjustment exercises using a COTS software package. Also reported are the results of post-launch experiments, study and evaluation of DEMs vis-à-vis ortho-images from Cartosat-1. [ABSTRACT FROM AUTHOR]

The present study demonstrates an unprecedented green process for the production of spherical-shaped Au and Ag nanoparticles synthesized and stabilized using a bacterium, Brevibacterium casei. Aqueous solutions of chloroaurate ions for Au and Ag(+) ions for silver were treated with B. casei biomass for the formation of Au nanoparticles (AuNP) and Ag nanoparticles (AgNP). The nanometallic dispersions were characterized by surface plasmon absorbance measuring at 420 and 540 nm for Ag and Au nanoparticles, respectively. Transmission electron microscopy showed the formation of nanoparticles in the range of 10-50 nm (silver), and 10-50 nm (gold). XRD analysis of the silver and gold nanoparticles confirmed the formation of metallic silver and gold. Further analysis carried out by Fourier Transform Infrared Spectroscopy (FTIR), provides evidence for the presence of proteins as possible biomolecules responsible for the reduction and capping agent which helps in increasing the stability of the synthesized silver and gold nanoparticles. The biological activities of the synthesized particles were confirmed based on their stable anti-coagulant effects. The use of bacterium for nanoparticles synthesis offers the benefits of ecofriendliness and amenability for large-scale production., (Copyright 2010 Elsevier B.V. All rights reserved.)