Debajyoti Dhar

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.

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.

Modeling temporal growth profile of vegetation index from Indian geostationary satellite for assessing in-season progress of crop area

Highlights In-season agricultural area tracking at regular interval from geostationary satellite. Modelling of temporal profile of vegetation index spread across two consecutive agriculture seasons to track crop area. The crop area estimates and their frequent updates in an agricultural growing season are essential to formulate policies of country’s food security. A new methodology has been developed with high temporal vegetation index data at 1000 m spatial resolution from Indian geostationary satellite (INSAT 3A) to track progress of country-scale rabi (post-rainy) crop area in six agriculturally dominant states of India. The 10-day (dekad) maximum normalized difference vegetation index (NDVI) composite products at 0700 GMT (Greenwich Mean Time) were generated and used in the study. A cubic function was fitted to NDVI temporal profile on the training data-sets of 2009–2010. Model parameters were standardized over 40 agroclimatic subzones, which were used to estimate rabi crop area at 10-day interval in the next two seasons. Uncertainties in the model, in terms of days, were found to be less than (3–8 days) compositing period. The INSAT-based estimates showed –18.1% to 14.6% deviations from reported rabi crop area. Subpixel heterogeneity was found to be the major cause for the delay in crop area tracking in study region. The interseasonal variability in the estimate was consistent with the reported statistics with a correlation coefficient of 0.89. A comparative study showed that INSAT estimated rabi area had 16.36% deviation from high spatial resolution AWiFS (Advanced Wide-Field Sensor)-estimated area at 2 km × 2 km grid over ground observation points. It is recommended that high temporal NDVI product with finer spatial resolution satellite would, by offsetting the impact of subpixel heterogeneity, enable improved country-scale crop area monitoring.

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.

High dynamic range processing of Mars Color Camera images

High dynamic range processing is one of the desired step for images taken with varying exposures for the same area to capture details in dark shadows as well as bright light. Indian Mars Orbiter Mission carrying Mars Color Camera (MCC) has the capability of taking images with varying exposures. Images taken by this camera during Earth Bound Phase of the flight is processed for High Dynamic Range (HDR). Satellite images are good as well as interesting candidate for high dynamic range processing. Low dynamic range (LDR) images needs to be registered a priori, which itself is a challenging task as these images may have cloud pixels, noise, low contrast etc. Significant improvements observed after HDR processing is statistically and visually depicted in this paper as compared to single LDR frames taken by the camera.

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.

Minnaert Topographic Correction of Mars Color Camera Images from Mars Orbiter Mission

Mars Color Camera (MCC) images obtained from Mars Orbiter Mission (MOM) are gaining scientific popularity since MOM insertion into an elliptical orbit around Mars on 24th Sep, 2014. Planetary remotely sensed images are corrected for topographic effects to normalize the radiance measures before considering the data for science analysis. It is proposed here to use non Lambertian Minnaert semi empirical approach for correcting MCC images that are used for deriving results for Mars surface science. The methodology outlined here uses terrain parameters such as slope and aspect derived from Mars Orbiter Laser Altimeter (MOLA) digital elevation model (DEM). Topographically corrected images were evaluated for the improvement in its radiometry quantitatively and it is found to reduce topographic shading and improve the image quality.

Comparative analysis of demosaicking techniques for Mars Colour Camera data

A digital camera captures the light that falls onto the sensor. Imaging begins with the source of light called illumination and all illuminations virtually consist of light with multiple wavelengths. The captured object can be represented either as panchromatic image or multispectral image. In multispectral imaging, each pixel holds an intensity values different for different spectral resolution. In this imaging multiple bands are realized using field splitting technique near focal plane. The final selection of the spectral bands is achieved by using appropriate band pass filters in front of the detectors. But with the advancement of time and technology in order to reduce cost and size, single sensor with color filter arrays (CFA) has introduced and improved. The most common array is the Bayer color filter array, where the green image is measured at a higher sampling rate because the peak sensitivity of the human visual system lies in the medium wavelengths, corresponding to the green portion of the spectrum. Most of the digital sensors are “Bayer Sensor”. A few digital cameras have a relatively new type of sensor that use the principle of depth of penetration and capture three color values at each pixel. In case of Bayer, individual pixel will have single spectral information at a time depending on the colour filter of the pixel. The reconstruction of the full color image requires each pixel must have R, G and B values, however typically only one color is sampled at a particular spatial location. A subsequent interpolation step is needed which is commonly called “Demosaicking” [2]. The process of demosaicking introduces various artifacts commonly false coloring, zippering effect. These artifacts are very less in inter-channel interpolation, but it changes the output values of the RGB channels. In Data Processing chain visual quality and value of the corresponding digital number (DN) which is being used in physical unit (radiance) conversion should not be deviated much from the original raw data. These three methods are taken from the broader categories of demosaicking. Comparative analysis is carried out.

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.