R. Ramakrishnan

An automatic satellite image registration technique based on Harris corner detection and Random Sample Consensus (RANSAC) outlier rejection model

Automatic satellite image registration is a challenging task of overlaying two images for geometric conformity aligning common features by establishing a transformation model using distinguishable feature points collected simultaneously in both the images in a completely un assisted manner. Remote sensed images capture terrain features in a natural condition subjected to seasonal changes, sun illumination conditions, and cloud presence. The critical steps in image registration are collection of feature points and estimating a spatial transformation especially when outliers are present besides feature matching and resampling the slave image to the master image geometry. In this paper, the details and merit of employing automatic Harris corner detection and building a transformation model using Random Sample Consensus (RANSAC) algorithm is brought out while registering a pair of LISS-3 or AWIFS images from Indian Remote Sensing Satellite (IRS) platform. Potential available with this approach for performing large scale image registration tasks such as time series processing are highlighted.

Adaptive stochastic gradient descent optimization in multi temporal satellite image registration

Image registration of satellite imagery is challenging task as the image pair may have been captured by different sensors, view angles or at different times. Conventional satellite image registration tasks are achieved by feature based techniques which are to be identified and matched before estimating transforms and resample the moving or slave image to a fixed or master image. However, automatic image registration is very important requirement for voluminous data sets. Even if feature selection made automatic, even distribution and the density of feature points is always not ensured. A good alternative is to switch over to intensity based image registration techniques which are somewhat image content independent. We bring out details on an intensity based multi temporal satellite image registration task with metric, transform and optimization components based method. An optimization technique employed to find optimal transform parameters by maximizing the chosen similarity measure criteria, provides a robust image registration framework. Adaptive Stochastic Gradient Descent optimization (ASGD) for satellite image registration is a promising new technique, details of which are reported here.

An efficient algorithm for automatic fusion of RISAT-1 SAR data and Resourcesat-2 optical images

Satellite Image fusion generates single hybrid image from a collection of input satellite images and helps us to extract maximum information from the remotely sensed datasets to achieve optimal spatial and spectral resolution. The critical steps of image fusion framework are co-registration of Synthetic Aperture Radar(SAR) data with corresponding optical scene, enhance the images for visual clarity and then merge the multi sensor data with a standard fusion technique. The image fusion system should perform all these steps in an automatic manner for providing ease to the user. The primary attention of this work is to examine the improvement that can be obtained by fusion of low resolution multi spectral data obtained from optical Resourcesat-2 platform (LISS-4MX/LISS-III/AWIFS Sensor having 5m/24m/56m spatial resolution) with high resolution RISAT-1 (Fine Resolution STRIPMAP (FRS-1)/Medium Resolution SCANSAR(MRS) mode data having 3m/18m spatial resolution) using SAR-Optical image fusion system discussed above. This integration of optical and SAR images from Indian Remote Sensing satellites facilitates better visual and automatic image interpretation. The Maximum Likelihood algorithm is used for classification of fused image and Resourcesat-2 multispectral data. The quality improvement of the fused product can be observed by comparing the classification accuracies of merged data with original multispectral data of the same region.

Kernel based learning approach for satellite image classification using support vector machine

Machine learning is a scientific computing discipline to automatically learn to recognize complex patterns and make intelligent decisions based on the set of observed examples (training data). Support Vector Machine (SVM) is a supervised machine learning method used for classification. An SVM kernel based algorithm builds a model for transforming a low dimension feature space into high dimension feature space to find the maximum margin between the classes. In the field of geospatial data processing, there is a high degree of interest to find an optimal image classifier technique. Many image classification methods such as maximum likelihood, K-Nearest are being used for determining crop patterns, land use and mining other useful geospatial information. But SVM is now considered to be one of the powerful kernel based classifier that can be adopted for resolving classification problems. The objective of the study is to use SVM technique for classifying multi spectral satellite image dataset and compare the overall accuracy with the conventional image classification method. LISS-3 and AWIFS sensors data from Resourcesat-1, Indian Remote Sensing (IRS) platform were used for this analysis. In this study, some of the open source tools were used to find out whether SVM can be a potential classification technique for high performance satellite image classification.

GEOID: GRID Services for Earth Observation Image Data Processing

Abstract Recent developments in space technology and exponential increase in demand of earth observation data from space have generated a requirement of a data processing environment, where users can discover the data and process, based on their requirements. Grid Services for Earth Observation Image Data Processing (GEOID) is proposed with a motivation to cater to future earth observation applications requirements of digital earth. This paper discusses the overview of the GEOID architecture, its deployment scenario, use-cases and simulation results. Core technologies used for implementation include Grid computing and Service Oriented Architecture. GEOID provides capability to address requirements of applications such as real-time monitoring, time series data processing and processing with user required quality to meet the requirements of end user applications. GEOID allows users to access the archive products or the raw satellite data stream and process their area of interest. Simulations show that applications such as time series analysis show considerable improvement in processing time by using GEOID.

Resourcesat (IRS-P6) value added data products

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.

Performance Evaluation of Satellite Image Resampling Service on Distributed Operating System

In this paper, we carry out the performance evaluation of satellite image resampling software, which follows service-oriented architecture and the service executes on a distributed operating system. The parameters, which are used for performance evaluation, include load balancing under multiple instance execution, overheads of process migration, effect of file caching and replication and performance of resampling service under different file access modes.

An automated seamless mosaicing system of multi-charge coupled devices of panchromatic data

ABSTRACTPanchromatic data of pixel resolution 5.8 m obtained from IRS-1C and IRS-1D satellites proved to be very useful for mapping purposes. One of the popular data product is the 70 km swath mosaic which is covered by a combination of 3 CCD line sensors, each with 4096 pixels. Each CCD-line sensor with different imaging times causes geometric problems of mosaicing three strips data together. In this paper, we propose the details of the design elements of system that caters to the need for accurate and automatic multi strip image registration without any second resampling of the data. The systematic geometric correction grid mapping is improved to facilitate accurate mosaicing by automatic image registration task that makes use of the overlap data within image strips and image registration is achieved up to sub-pixel level.

Improved image mosaicing technique using marker controlled watershed segmentation

Seamless mosaic generation is a challenging issue in the field of image processing. Multi date and multi time images vary in terms of radiometry as well as geometry of viewing, this makes the task at hand more intricate. These variations are taken care of with the help of radiometrie normalization and geo-correction. In this paper an approach is proposed for mosaicing geo corrected images acquired on different days. This approach uses inter scene normalization of the images and utilizes a mathematical morphological operator to find out the best possible seam line from the overlap area. Overall visual quality of the mosaiced product is analyzed to verify the algorithm.

Resourcesat-2 image restoration for differential pulse code modulation compression artifacts

Remote sensing satellites use onboard compression techniques to overcome the limited bandwidth and increasing data volume requirements of images. On-board compression using Differential Pulse Code Modulation (DPCM) is implemented for Resourcesat-2, LISS-3 and LISS-4 sensors. Implemented DPCM is a lossy compression and hence renders artifacts, when images are decompressed on ground. In this paper a technique for restoration of DPCM artifacts is proposed and its performance is evaluated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Proposed technique is used as part of operational data products generation software and sample results are shown.