Indian Remote Sensing Satellite Images Research Articles

Performance analysis of hyperspherical colour sharpening method for IRS satellite images

Various image fusion methods have been developed and investigated for different remote sensing (RS) applications. Hyperspherical Colour Sharpening (HCS) method was recently proposed for World View-2 imagery. A limited study has been carried out to find the performance of HCS method for other datasets. In this paper, an experiment is engineered in which HCS method was applied on Indian remote sensing (IRS) datasets. The performance analysis of the method was carried by both qualitative and quantitative methods. In addition to that the quality of indices image for each method is compared to analyse the suitability of methods for various applications based on these indices. Brovey transformation (BT), principal component substitution (PCS), high pass filtering (HPF) and discrete wavelet transform-based principal component substitution (DWT-PCS) were also applied on the selected data and used in comparative analysis with the HCS method. The study reveals that HCS method outperforms in terms of the spectral fidelity, but produces some shortcoming for spatial resolutions. The quality of indices images show that BT and HCS methods do not hold the spatial details after fusion indices image computation, while indices from HPF, DWT-PCS and PCS hold some of spatial information injected into fused output.

A Unified Software Framework for Automatic Precise Georeferencing of Large Remote Sensing Image Archives

Though georeferencing satellite images is a solved industry problem long back, every satellite sensor data is unique to process due to its proprietary information and expected geometric accuracies usually demands use of reference data. Industry is moving towards unifying frameworks, sensor models where the users have to just plug-in details. The growing archive is inevitable and therefore (re) processing requirements have to handle huge image archives with state of art georeferencing standards automatically. Use of reference data is necessary to georeference the data sets to better accuracies beyond systematic corrections. This paper presents an approach for such tedious task of automatically reliably georeference huge image archives involving reference image/control points use in georeferencing Indian Remote Sensing satellite images.

Efficient parallel algorithm for pixel classification in remote sensing imagery

An important approach for image classification is the clustering of pixels in the spectral domain. Fast detection of different land cover regions or clusters of arbitrarily varying shapes and sizes in satellite images presents a challenging task. In this article, an efficient scalable parallel clustering technique of multi-spectral remote sensing imagery using a recently developed point symmetry-based distance norm is proposed. The proposed distributed computing time efficient point symmetry based K-Means technique is able to correctly identify presence of overlapping clusters of any arbitrary shape and size, whether they are intra-symmetrical or inter-symmetrical in nature. A Kd-tree based approximate nearest neighbor searching technique is used as a speedup strategy for computing the point symmetry based distance. Superiority of this new parallel implementation with the novel two-phase speedup strategy over existing parallel K-Means clustering algorithm, is demonstrated both quantitatively and in computing time, on two SPOT and Indian Remote Sensing satellite images, as even K-Means algorithm fails to detect the symmetry in clusters. Different land cover regions, classified by the algorithms for both images, are also compared with the available ground truth information. The statistical analysis is also performed to establish its significance to classify both satellite images and numeric remote sensing data sets, described in terms of feature vectors.

Coseismic Horizontal Offsets and Fault-Trace Mapping Using Phase Correlation of IRS Satellite Images: The 1999 Izmit (Turkey) Earthquake

On August 17, 1999, a strong earthquake (Mw ? 7.4) occurred along the western sector of the North Anatolian Fault system in Turkey. The epicenter was located near the city of Izmit, 50 km east of Istanbul. Previous works determined the coseismic surface displacements by satellite synthetic aperture radar (SAR) interferometry (InSAR) and satellite optical-image correlation. In 1999, the highest spatial resolution orbiting camera was the panchromatic sensor (PAN), a 5.8-m pixel sensor (SPOT 2 was a 10-m pixel sensor) onboard the Indian Remote Sensing (IRS) satellite. We propose to apply a new phase-correlation method to PAN images to study the coseismic rupture due to the Izmit earthquake. The phase-correlation method does not need phase unwrapping and was proved to be robust under a wide variety of circumstances. Image correlometry deals with the quantification of the subpixel offsets over the whole image, allowing displacement measurement with an accuracy that is proportional to the pixel size. We measured the near-field deformations exploiting two geometrically corrected IRS images with similar look angles. A quality check of the derived offset map was performed by comparison with GPS benchmarks and SPOT offsets. The results show that IRS PAN images can be correlated to derive coseismic slip offsets due to a large earthquake (and to map its fault trace).

Application of a New Symmetry-Based Cluster Validity Index for Satellite Image Segmentation

An important approach for image segmentation is clustering pixels based on their spectral properties. In particular, satellite images contain land cover types, some of which cover significantly large areas, while some (e.g., bridges and roads) occupy relatively much smaller regions. Automatically detecting regions or clusters of such widely varying sizes presents a challenging task. In this letter, a symmetry-based cluster validity index, named Sym-index (Symmetry distance-based index), is proposed. It is able to correctly indicate the presence of clusters of different sizes as long as they are internally symmetrical. A genetic-algorithm-based clustering technique that optimizes the Sym-index is used for image segmentation where the number of clusters is determined automatically. The superiority of the proposed index, as compared to other indices, is established for automatically segmenting the land cover types from SPOT and Indian Remote Sensing satellite images of two different cities in India.

Census of Natural Resources with Earth Observation and GIS: A Proto‐type from India

Census is the count or inventory of specific phenomenon, such as population, wild life, industry, livestock etc. Another aspect is that census is carried out periodically to show the changing trends of the phenomenon and state. Space based imaging systems, from their vantage position, has unambiguously demonstrated their capability in providing vital information on natural resources. With the availability of high‐resolution multi‐spectral and panchromatic earth observation images and geographic information system, can we now consider a natural resources census? The census of natural resources ‐ land, water, soils, forests and other elements conducted in a systematic manner and with a repeat cycle to depict changes and modifications can provide a “snap‐shot” of a country's status of natural resource at a particular moment of time and help in identifying areas of rapid and significant changes, which can be monitored in detail. Such inventory with periodic monitoring would help in managing the natural resources and will provide inputs to meet the demands of large population in terms of more food production, more energy generation, water management, better civic amenities, more infrastructure built‐up and increasing per‐capital expenditure for maintaining a quality of life. In India, Natural Resources Census (NRC) programme has been conceptualized and is being implemented. The NRC will use Indian Remote Sensing satellite images and prepares natural resources information layers of 7 key themes (land use/land cover, soil, geomorphology, vegetation, snow/glacier, land degradation, wetlands) at 1:50,000 scale with the periodicity of 5-20 years for different themes. The NRC maps and data support the repository of natural resources information and contribute to the operationalisation of a spatial data infrastructure (SDI). Recognising that standardization of methodology for mapping and spatial database generation to enable NRC for SDI is important, few proto‐type NRC studies has been conducted. This paper discusses the concept of a nation‐wide NRC programme and the results of one of the proto‐type studies (Bangalore urban district) that India has carried out. The concept, methodology and standards suggested for NRC will be certainly useful for any country.

Absolute position estimation using IRS satellite images

This paper presents an absolute position estimation method using an Indian remote sensing (IRS) satellite images, which is a part of a position estimation system. The accumulated buffer (AB) matching method is proposed, in which a set of accumulator cells is employed for a fast edge-based matching between IRS satellite images and aerial images. The proposed algorithm is based on the accumulated buffer, which is a set of accumulator cells employed in the Hough transform widely used for robust detection of lines or curves. Thanks to the inherent robustness of the proposed algorithm, accurate registration can be accomplished between totally different image modalities. By computer simulations with two sets of real aerial image sequences, the performance of the AB matching method is analyzed and its effectiveness is shown in terms of the position error in the hybrid position estimation system. We found that the proposed AB matching method significantly reduces the average position error produced by the relative position estimation in the hybrid position estimation system.

A new gray level based Hough transform for region extraction: An application to IRS images

A technique using the Hough transform is described for detection of homogeneous line segments directly from (i.e., without binarization of) gray level images. A definition of “region” in terms of these line segments, with constraints on its length and variance, is provided. The algorithm is able to extract gray level regions irrespective of their shape and size. The effectiveness of the method is demonstrated on Indian Remote-sensing Satellite (IRS) images.

A prototype expert system for interpretation of remote sensing image data

Automated image interpretation systems of remotely sensed images are of great help in the present scenario of growing applications. In this paper, we have critically studied visual interpretation processes for urban land cover and land use information. It is observed that the core activity of interpretation can be described as plausible combinations of pieces of evidential information from various sources such as images, collateral data, experiential knowledge and pragmatics. Interpretation keys for the interpretation of standard false colour composites are considered to be tone/colour, pattern, texture, size, shape, association, relief and season. These interpretation keys encompass the spectral, spatial and temporal knowledge required for image interpretation. Our focus is on a knowledge-based approach for interpretation of standard false colour composites (fcc). Basic information required for a knowledge-based approach is of four types viz., spectral, spatial, temporal and heuristic. Generic classes and subclasses of image objects are identified for the land use/land cover theme. Logical image objects are conceptualised as region/area, line and point objects. An object-oriented approach for the representation of spectral and spatial knowledge has been adopted. Heuristic information is stored in rules. The Dempster-Shafer theory of evidence is used to combine evidence from various interpretation keys for identification of generic class and subclass of a logical image object. Analysis of some Indian Remote Sensing Satellite images has been done using various basic probability assignments in combination with learning. Explanation facility is provided by tracing the rules fired in the sequence.

Variable-Rate Block Interpolative Coding for Satellite Images

While many image compression schemes have come up in recent years, a new scheme using two-dimensional interpolation for satellite images is presented in this paper. This scheme is very useful for onboard application since it achieves reasonable compression with very simple encoding and decoding operation. Also the scheme, namely Block Interpolative Coding, has the facility for fixing the required compression, quality and distortion. Simulation results on Indian remote sensing satellite images have been presented.

Geomorphology and Petroleum Prospects of Cauvery Basin, Tamilnadu, Based on Interpretation of Indian Remote Sensing Satellite (IRS) Data

Geomorphological studies of the Cauvery basin, Tamilnadu were carried out using IRS images with special emphasis on identification of zones of hydrocarbon occurrences. The basin exhibits landforms of fluvial and fluvio-marine plains. Two major trends of lineaments and 15 circular anomalies have been identified. The NW-SE trend appears to be younger and might have played an important role in migration and entrapment of hydrocarbons. Six circular anomalies are associated with known oil/gas wells. The study has identified probable zones of hydrocarbon occurrences.