A. Senthil Kumar

Impact fragmentation of Lonar Crater, India: Implications for impact cratering processes in basalt

Impact fragmentation is an energetic process that has affected all planetary bodies. To understand its effects in basalt, we studied Lonar Crater, which is a rare terrestrial simple impact crater in basalt and analogues to kilometer-scale simple craters on Mars. The Lonar ejecta consists of basaltic fragments with sizes ranging from silt to boulder. The cumulative size and mass frequency distributions of these fragments show variation of power index for different size ranges, suggesting simple and complex fragmentation. The general shape of the fragments is compact, platy, bladed, and elongated with an average edge angle of 100°. The size distribution of cobble- to boulder-sized fragments is similar to the fracture spacing distribution in the upper crater wall, indicating the provenance of those large fragments. Its consistency with a theoretical spallation model suggests that the large fragments were ejected from near surface of the target, whereas the small fragments from deeper level. The petrophysical properties of the ejecta fragments reflect the geophysical heterogeneity in the target basalt that significantly reduced the original size of spall fragments. The presence of Fe/Mg phyllosilicates (smectites) both in the ejecta and wall indicates the role of impact in excavating the phyllosilicates from the interior of basaltic target affected by aqueous alteration. The seismic images reveal a thickness variation in the ejecta blanket, segregation of boulders, fractures, and faults in the bedrock beneath the crater rim. The fracturing, fragmentation, and fluvial degradation of Lonar Crater have important implications for Mars.

Wavelet Based Post Classification Change Detection Technique for Urban Growth Monitoring

Urban areas are the most dynamic region on earth. Their size has been constantly increased during the past and this process will go on in the future. Since there is no standard policy and guidelines for construction of buildings and urban planning, cities tend to have irregular growth. Many cities in the world face the problem of urban sprawl in its suburbs. So issues of urban sprawl need to be settled with the help of technologies such as satellite remote sensing and automated change detection. This paper presents a wavelet based post classification change detection technique that is applied to 1996 and 2004 MSS images of Madurai City, South India to determine the urban growth. The classification stage of the technique uses coilflet wavelet filter to correlate with the MSS land cover images of Madurai city to derive texture feature vector and this feature vector is inputted to a fuzzy-c means classifier, an unsupervised classification procedure. The post classification change detection technique is employed for identifying the newly developed urban fringe of the study area. The error matrix analysis is used to assess the accuracy of the change map. The performance of the presented technique is found superior than that of classical change detection methods such as image differencing, change vector analysis and principal component analysis.

Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously.

Information fusion in tree classifiers

The classification performance of a decision-tree classifier is strongly influenced by the classification strategy employed at each decision node and the presence of noise. In this study, multispectral data classification was attempted with maximum likelihood and backpropagation neural networks, as well as their combination. Three methods of fusing their information were studied in detail. The motivation for the information fusion was to enhance the interpretation of a particular pixel under study with the classifier that has a minimum uncertainty in assigning the pixel to one of desired classes. The classification performance with the fusion methods was found to be better than that of the individual classifiers. In addition, their recognizing ability in the presence of additive noise was found to be better, especially with the fusion using the fuzzy integral.

Expansion of LISS III swath using AWiFS wider swath data and contourlet coefficients learning

Spaceborne sensors have limited capability to acquire images with wider swath at high spatial and high temporal resolutions simultaneously. This study reports a ground processing technique that combines images from two sensors onboard Resourcesat-2 (RS2) Linear Imaging and Self-Scanning Sensor (LISS III) and an Advanced Wide-Field Sensor (AWiFS) to overcome this limitation. The spatial resolution of LISS III is 23.5 × 23.5m and that of AWiFS is 56 × 56m. The temporal resolution of LISS III is 24 days and that of AWiFS is 5 days. The 140-km swath of the LISS III overlaps at center portion of 740-km swath of AWiFS in simultaneous acquisition. Assume that the nonoverlapping region of the AWiFS contains similar Earth’s surface features of the LISS III overlapping region; then, it is possible to enhance the spatial resolution of AWiFS to the spatial resolution of LISS III in the nonoverlapping region. With this assumption, we propose a novel technique to enhance the spatial resolution of the nonoverlapping region through a single-image super-resolution technique using nonsubsampled contourlet transform (NSCT) and evaluated it on RS2 data-sets. The proposed method can create a synthetic image with 740-km swath at 23.5 × 23.5m spatial and 5-day temporal resolutions. Experimental results demonstrated that it outperforms the support vector regression (SVR)-based methods in prediction accuracy and computational time.

Restoration of high frequency details while constructing the high resolution image

Indian Remote Sensing (IRS) Resourcesat satellites have AWiFS, LISS-III, LISS-IV sensors of 56m, 23.2m, 5.8m spatial resolutions respectively and of having identical spectral and temporal (simultaneous acquisition) resolutions but they are different in spatial and radiometric resolutions. In this work, we are trying to create high resolution (HR) image from coarser resolution AWiFS image called as emulated LISS-III like HR image. It will have HR (original LISS-III) image characteristics and minimal Mean Squared Error (MSE), ideally zero difference with LISS-III image as refernce, which inturn processed for identical spatial resolution of LISS - III. Ground sampling distance (GSD) of AWiFS is equivalent to 2.4 pixels of LISS-III. Radiance values are compared for normalization purpose, after weighted averaging of LISS-III to create AWiFS equivalent spatial resolution. Spatial resolution can be enhanced by improving the spatial sampling density of the signal. Nyquist-Shannon sampling theorem directs the sampling rate for exact reconstruction of the signal. The Resourcesat images are pre-processed to match this condition. Papoulis-Gerchberg algorithm is suitably improved for constructing the HR image from AWiFS. The results are compared with the classical interpolation techniques such as nearest neighbor, bilinear, spline, cubic convolution Papoulis-Gerchberg and modified Papoulis-Gerchberg [1].

Geocoding RISAT-1 MRS images using bias-compensated RPC models

The purpose of this paper is two-fold. First, the use of the rational polynomial coefficients (RPCs) model is studied for geocoding of Medium Resolution Scan (MRS) ground range (GR) images from the RISAT-1 SAR mission. As the GR images are obtained after many preprocessing image corrections for topographic effect, range cell migration, etc., the number of ground control points (GCPs) required for orthorectification to meet desired geometric quality needs to be established. This assumes importance due to difficulty in visual identification of the GCPs in 18 m-resolution MRS SAR images. Second, three possible methods of bias-compensated RPC models are studied for geocoding. These cases are (A) modified RPC with shift bias, (B) regenerated RPC with shift bias, and (C) regenerated RPC with affine transform model. Experiments are carried out with a set of eight scenes acquired over planar regions especially to avoid the impact of SAR-specific geometric effects such as foreshortening and layover. Geometric accuracy of the orthoimages obtained from these cases is verified at GCPs used for processing as control points and at new GCPs used as check points. It is observed that the modified RPC with the shift bias case required more GCPs to meet the desired geopositioning accuracy. Even though both the regenerated RPC models have shown near similar performance, the regenerated RPC with shift bias compensation is found to reach the required geopositioning accuracy with least number of the GCPs, suggesting it as a strong candidate for realizing operational high precision RISAT-1 geocoded products for multi-temporal data analysis.

Fast spatiotemporal data fusion: merging LISS III with AWiFS sensor data

The high resolution of remote sensors has evolved to capture the fine details of the Earth’s surface features in remote-sensing (RS) data. There is a trade-off between this fine spatial resolution and the temporal resolution of global space-borne sensors. Global space-borne sensors are not good enough to acquire an image at fine spatial and high temporal (FSHT) resolutions simultaneously. In this article, we propose a computationally efficient technique to create a FSHT resolution image using a ground-based data processing system. Resourcesat-2, part of the Indian Space Research Organization’s (ISRO’s) mission, carries linear imaging self-scanners (LISS III and LISS IV) and an advanced wide-field sensor (AWiFS) on board. The spatial and temporal resolutions of LISS III are 23.5 m and 24 days and those of AWiFS are 56 m and 5 days, respectively. The proposed method creates a synthetic FSHT resolution image with 23.5 m spatial and 5 day temporal resolution. This method is referred to as ‘LISS III spatial and AWiFS temporal’ (LSAT) data fusion. The LSAT data fusion method is based on a sub-pixel relationship between the images of a single AWiFS–LISS III image pair, which was acquired before or after the ‘prediction date’: a synthetic LISS III image for the time is ‘predicted’ (synthesized) using an AWiFS image at time and a single AWiFS–LISS III image pair at time , where . The LSAT model was tested on simulated and real data sets acquired by the LISS III and AWiFS sensors. The proposed method was compared with the recently developed spatiotemporal data fusion methods. The experimental results demonstrate that the method is computationally efficient and shows consistent prediction accuracy in retrieving surface reflectance changes.

High Spatial and Spectral Details Retention Fusion and Evaluation

To retain spatial and spectral details simultaneously from source images is a trade-off in image sharpening. Fourier and wavelet transform based image fusion methods retain better spectral quality but represent less spatial details as in source images. Wavelets and Laplacian pyramid perform well only at linear discontinuities because they do not consider the geometric properties of structures and do not exploit the regularity of edges. In this paper we proposed a novel image fusion method to preserve high spatial and spectral details based on the curvelet transforms. Curvelet transforms overcome the difficulty to identify the critical transient features. To retain high spatial details, finer and detailed scale coefficients of High Resolution (HR) PAN image are substituted in Low Resolution (LR) multispectral (MS) bands in the Fast Discrete Curvelet Transforms (FDCT) domain. Spectral details in the fused image are preserved by following the shape of the spectral reflectance curve of each pixel in the resample MS image. Spectral profile of the each pixel in spatially fused image is parallel to the spectral profile of the corresponding pixel in the resampled MS image. For experimental study of this method, Indian Remote Sensing (IRS) Resourcesat-1 LISS IV images are used as LR MS image and Cartosat-1 images are used as HR PAN Image. Proposed fusion method is evaluated against state of the art fusion techniques and quality measures.

Shunt active filter using Cuckoo search algorithm for PQ conditioning

In recent days the power quality problems (PQ) are more produced in power electronic devices and it creates unbalance loading, current harmonics and voltage unbalancing problems due to increase in reactive power. For compensating the reactive power various types of power devices are used. The shunt active power filter (SAPF) is one of the devices that are used for decreasing the PQ. To reduce the PQ problems, the performance of SAPF must be improved. In this paper, a controller is proposed for improving the performance of SAPF and compensating the PQ. The Cuckoo search (CS) algorithm technique which is used to get the reference current for compensating reactive power and reducing the PQ problem. The SAPF is used for reducing the harmonic disturbances. The dc link voltage is maintained as constant by using the proposed controller technique. Subsequently, the FFT analysis is used for measuring THD values of the proposed system. The proposed method is implemented in MATLAB/Simulink and its performance is evaluated. The performance of the proposed method is evaluated and compared with the PI controller and Fuzzy controller.