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This paper presents a novel and efficient approach for the generation of georeferenced (GEO) image from Single Look Complex (SLC) image and a procedure for automatic conversion of control points from GEO to SLC image. Using the proposed approach, the control points in one image can be transformed to the corresponding control points in other image with subpixel accuracy. Automatic transformation of control points from GEO to SLC domain is an essential requirement in many of the Geometric Data Quality Evaluation (GDQE) and Radiometric Data Quality Evaluation (RDQE) activities. One of the main reason for control point transformation is that it is difficult to identify control points in SLC image due to its orientation, distortion and geometry of slant range SAR data. The proposed approach is also made operational in ISRO’s Data Quality Evaluation (DQE) software and is extensively applied for GDQE/RDQE evaluations on SAR data products. [ABSTRACT FROM AUTHOR]

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]

In this article, we establish an update procedure for rapid positioning coefficients or rational polynomial coefficients (RPCs) via iterative refinements using polynomial augmentation and reference images. RPCs are widely popular in establishing a ground-to-image relationship without using physical sensor model. However, the accuracies of RPCs are degraded due to unavoidable errors in physical sensor model based on colinearity conditions. These inaccuracies essentially arise due to undulating terrain, residual errors in attitude parameters, viz. roll, pitch and yaw, inexact modeling of drift and micro-vibration, orbit error, etc. In the paper, first an initial estimate of RPCs is obtained by using L2-regularized least square estimation. Subsequently, the RPCs are refined by using iterative affine augmentation. The RPC accuracy is further improved by a second-order polynomial augmentation. The results show that with the improved RPCs the average scan and pixel errors are within 0.5 pixel. The results of the paper are employed and validated on Resourcesat-2 imagery. [ABSTRACT FROM AUTHOR]

This article presents a novel approach for estimation of the residual attitude of a remote-sensing satellite based on satellite images with ancillary information and ground control points (GCPs). First, a non-linear model which translates the residual errors in roll, pitch, and yaw to scan-errors and pixel-errors in the image space is established. Subsequently, using the model and given scan-errors and pixel-errors at GCPs, an estimate of residual roll, pitch, and yaw based on the least square minimization of residuals in conjunction with the Newton's method for non-linear optimization is proposed. A simulation is carried out to show that the estimates of residual roll and pitch are within 0.0008° (equivalent to 0.5 pixel) and the residual yaw is within 0.015° (equivalent to 0.75 pixel at the extreme ends) to its true values. The results of the article can be applied to determine the residual attitude of any remote-sensing satellite. We demonstrate our results by estimating the residual attitude based on the data-products of various Indian remote-sensing satellites. The effectiveness of the approach is shown by comparing the results with that of existing technique and it is concluded that the presented technique estimates residual attitude more accurately than the existing method. [ABSTRACT FROM AUTHOR]

The single-frequency Radar Imaging Satellite-1 (RISAT-1) Synthetic Aperture Radar (SAR) (launched in April, 2012) is designed to provide a wide variety of imaging options compared to the prevailing SAR missions. The scale of the calibration task for the RISAT-1 mission is increased with the inclusion of multiple beams and polarizations (single, dual and quad) along with circular/hybrid polarization capabilities, for the first time from space. Significant efforts have been made in the evolution of SAR processor for the provision of radiometrically and geometrically calibrated products to the users. After the commissioning phase, RISAT-1 was put into routine operations in Oct. 2012. Since then extensive beam coverage of Amazon rainforest in various modes was carried out to evaluate the calibration parameters and SAR processor performance. Subsequently, the processor version 1.2.0 was improved to version 1.3.0 and with further modifications to version 1.4.0. This article details the analysis carried out at each processor version, by quality evaluation team, to quantify the parameter behaviour and to give feedback on the processor performance to the SAR processor team. The various radiometric quality parameters evaluated and monitored are shape of the antenna pattern, gamma-naught, backscattering coefficient (sigma-naught), speckle index, radiometric resolution and Noise Equivalent Sigma Zero (NESZ). Data studied for the analysis is in Single Look Complex (SLC) for stripmap mode and multilook (Ground range) for ScanSAR and stripmap mode in dual and circular polarizations. The performance of version 1.4.0 for all the beams (near-to-far) is found to be consistent and meeting the mission specifications for the product. [ABSTRACT FROM AUTHOR]

The present study investigates two natural sites for calibration of IRS sensors, one at Thar Desert, Rajasthan and another at Rann of Kutch, Gujarat, India. The variance analysis of satellite data of different seasons over these sites and atmospheric characterization were carried out. Results of the study suggest that the sites identified are suitable and can be used for vicarious calibration of satellite sensors during the months of October to March as during this period the atmospheric loading of aerosols, water vapour and ozone are at minimum. Thar desert and Rann of Kutch sites show spatial uniformity of within ~0.78-2.0 % and 1.96-3.90 % respectively. Error budget is also estimated. If well calibrated instruments are used, the overall error should not exceed ~2 % for the reflectance based approach. [ABSTRACT FROM AUTHOR]

Data quality evaluation of synthetic aperture radar (SAR) products is essential with respect to mission performance and reliability of the data products for users. The generation of any value-added SAR product starts with a level-0 raw data product, so it is vital firstly to analyse and monitor the raw data. In other words, any inconsistency or error occur because of data, the SAR processor requires to ‘quality evaluate’ the raw data first to ensure that the higher level of data products meets the user specifications. Many parameters can be evaluated from the raw dataper sebefore SAR processing. Here, an independent approach is formulated to evaluate the long-term stability and performance of the SAR system from raw data. Quality parameters such as echo data statistics in terms of bias in the mean of Inphase (I) and Quadrature in phase (Q), power imbalance, phase imbalance, antenna pattern in elevation for the qualified swath, Doppler centroid estimation, and chirp phase and amplitude stability were evaluated and monitored for various scenes of an orbit and characterized in detail for different beams covering the near to far range. In the Radar Imaging Satellite (RISAT-1), the SAR beam is formed by a number of transmit/receive modules (TRMs) (total number of active TRMs depends on the look angle), and the consolidated effect of TRMs can be observed from the raw data instead of individual TRMs, which enables examination of the overall behaviour of beams, from near to far range in different orbits, for temporal stability. In this article, for the first time, a novel approach is taken to observe and evaluate the antenna pattern for the 3 dB qualified swath from range-uncompressed raw data and to relatively monitor the different beams from level-0 raw data for the qualified swath, for both extended homogeneous and non-homogeneous targets. Furthermore, the approach is justified in that it is not necessary to perform range compression, and also one can use scenes with heterogeneous targets to estimate and monitor the beamwidth parameter of the antenna pattern. This article focuses on the results obtained from RISAT-1 level-0 raw data from various beams covered in strip-map mode. The methodology to evaluate the RISAT-1 SAR system from raw data is discussed. Analysis is carried out with respect to different beams (60 beam data of different orbits either side, covering near to far nadir swath coverage) of fine resolution strip-map-1 (FRS-1) mode in circularly transmitted and linearly received polarization, with a defined swath of 25 km with 3 m × 2 m (azimuth × range) resolution. The results of evaluation and monitoring analysis show consistency in the identified parameters observed over time for different beams, and they are within specifications. [ABSTRACT FROM PUBLISHER]

This work proposes a neuro-fuzzy method for suggesting alternative crop production over a region using integrated data obtained from land-survey maps as well as satellite imagery. The methodology proposed here uses an artificial neural network (multilayer perceptron, MLP) to predict alternative crop production. For each pixel, the MLP takes vector input comprising elevation, rainfall and goodness values of different existing crops. The first two components of the aforementioned input, that is, elevation and rainfall, are determined from contour information of land-survey maps. The other components, such as goodness values of different existing crops, are based on the productivity estimates of soil determined by fuzzyfication and expert opinion (on soil) along with production quality by the Normalized Difference Vegetation Index (NDVI) obtained from satellite imagery. The methodology attempts to ensure that the suggested crop will also be a high productivity crop for that region. [ABSTRACT FROM AUTHOR]

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]

This paper presents a computationally efficient method for stereo image matching of remote sensing images. Traditional correlation methods often fail to give correct matching when the slope of the terrain is very high. A novel technique called aspect-based estimation is proposed here to find the best match points and to estimate the slope of the terrain. Stereo image matching is implemented in a hierarchical manner using wavelets to reduce the computational cost. A robust technique for identifying outliers based on local statistics is also presented here. Experimental results with CARTOSAT-1 images indicate that the aspect-based correlation and blunder detection works very efficiently and effectively in stereo image matching of remote sensing images. It is observed that this algorithm is able to give a high density of reliable match points of the order of 1 for every 5 × 5 pixels. The technique is demonstrated here for CARTOSAT-1 images. However, the proposed algorithm can be applied to a remote sensing stereo pair of any spaceborne imagery. [ABSTRACT FROM AUTHOR]

Remote sensing data products need to meet stringent geodetic and geometric accuracy specifications irrespective of intended user applications. Georeferencing is the basic processing step towards achieving this goal. Having known the imaging geometry and mechanism, the mathematical models built with the use of orbit and attitude information of the spacecraft can correct the remote sensing data for its geometric degradations only up to system level accuracy (IRS-P6 DP Team, 2000). The uncertainties in the orbit and attitude information will not allow the geometric correction model to generate products of accuracy that can meet user requirements unless Ground Control Points (GCP) are used as reference geo-location landmarks. IRS-P6 data processing team has been entrusted with developing a software system to generate data products that will have desired geodetic and geometric accuracies with known limitations. The intended software system is called the Value Added Data Products System (VADS). Precision corrected, Template Registered, Merged and Ortho Rectified products are the value added products planned with VADS. [ABSTRACT FROM AUTHOR]

This work deals with multisensor data fusion to obtain landcover classification. The role of featurealevel fusion using the Dempster-hafer rule and that of data-level fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated and classification ace curacy for the test data is evaluated. Two examples of data fusion of optical images and a synthetic aperture radar image are presented, each set having been acquired on different dates. Classification accuracies of the technique proposed are compared with those of some recent techniques in literature for the same image data. [ABSTRACT FROM AUTHOR]

Presents a study that suggested a segmentation approach to classification of multispectral image in Markov random field (MRF) frame work. Outline of the framework of MRF on region adjacency graph; Description of the segmentation scheme based on MRF; Cluster validation scheme and the implementation details of the proposed algorithm.

The use of cubic spline wavelets for merging high spatial content of panchromatic (PAN) data and spectral contents of multispectral (LISS-3) data of IRS-1C/1D spacecraft is proposed here. It is shown that the proposed method preserves the spatial content of the original PAN data and the spectral content of the LISS-3 better than many of the conventional approaches. It is also suggested that the spatial content of the merged data can further be enhanced by first correcting the PAN data for overall modulation transfer function (MTF) of the sensor. The overall MTF was realized with a piecewise linear model and using the sensor specified MTF at nyquist frequency. [ABSTRACT FROM AUTHOR]