Indian Institute of Remote Sensing

About: Indian Institute of Remote Sensing is a government organization based out in Dehra Dūn, India. It is known for research contribution in the topics: Land cover & Normalized Difference Vegetation Index. The organization has 756 authors who have published 1355 publications receiving 16915 citations. The organization is also known as: Indian Photo-interpretation Institute.

Target detection algorithm for airborne thermal hyperspectral data

Abstract: . Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm−1 to 1280 cm−1 region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm−1 (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.

Digital documentation, bridge deck linearity deformation and deck thickness measurement using terrestrial laser scanner (tls) and close range photogrammetry (crp)

Abstract: . Bridges are one of the vital and valuable engineer structure from decades. As they play a major role in the road transportation sector. Few old bridges lacks its documents about the measurements of the structure. The study has been carried out on three different types of bridges like Truss, Beam and Cable bridges. Documenting these bridges can be utilised to reconstruct or renovate the bridge in case of any disaster or damage. 3D documentation is made from the point cloud Dataset acquired from Terrestrial Laser Scanner – TLS (Riegl VZ 400) and Close Range photogrammetry – CRP (Nikon DSLR 5300). TLS and CRP point cloud are merged together to increase the density of points. Over the duration of time the bridge gets older and due to the load on the bridge deck, linearity in the deck effects and this linearity deformation measurement is important to know the present deformation in the deck. To know exactly at which part there is more linearity deformation, deflection is calculated at sample intervals between the present linearity conditions of the deck to the idle linearity conditions of the deck. The bridge deck thickness is also measured with the point cloud dataset. A slice is cut through the deck of point cloud dataset, the difference between the top and bottom layer of the deck gives us the thickness of the deck including the road. This thickness can be used to measure when a new deck layer is constructed or during filling up of any potholes. This study is mainly focused to help the construction and maintenance authority, bridge monitoring department and researchers.

3-Dimensional modeling of 2014-Malin Landslide, Maharashtra using satellite-derived data: A quantitative approach to numerical simulation technique

Abstract: . Debris flows, a type of landslides, are not nowadays limited only to the periodic devastation of the geologically fragile Himalaya but also ubiquitous in weathered Deccan Volcanic Province of the cratonic south Indian peninsula. Comprehensive assessment of landslide hazard, pertinently, requires process-based modeling using simulation methods. Development of precipitation triggered debris flow simulation models of real events are still at a young stage in India, albeit, especially in tectonically less disturbed regions. A highly objective simulation technique has therefore been envisaged herein to model the debris flow run-out happened in Malin. This takes cues from a high- resolution DEM and other ancillary ground data including geotechnical and frictional parameters. The algorithm is based on Voellmy frictional (dry and turbulent frictional coefficients, μ and ξ respectively) parameters of debris flow with pre-defined release area identified on high-resolution satellite images like LISS-IV and Cartosat-1. The model provides critical quantitative information on flow 1) Velocity, 2) Height, 3) Momentum, and 4) Pressure along the entrainment path. The simulated velocity of about 16 m/s at mid-way the slide plummeted to 6.2 m/s at the base with intermittently increased and decreased values. The simulated maximum height was 3.9 m which gradually declined to 1.5 m near the bottom. The results can be beneficial in engineering intervention like the construction of check dams to digest the initial thrust of the flow and other remedial measures designed for vulnerable slope protection.

Application of ASTER Remote Sensing for Lithological Mapping in the Udaipur District of Rajasthan, India

Abstract: Remote sensing applications for earth studies such as lithological discrimination, geological mapping and potential mineral exploration have shown great success worldwide. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level-1B image includes visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands that have been analysed to discriminate lithology features in meta-sedimentary terrains of Aravalli Supergroup in Udaipur area of Rajasthan, India. The area comprises various types of geological settings and rock types composed of economic valuable deposits of lead, zinc, copper, micas and marbles; they show spectral reflectance distinctly in bands of VNIR and SWIR. The unique spectral signature reflected by lithological unit shows effectiveness in lithological mapping. The reflectance spectra of various rock types, namely, phyllitic dolomite, siliceous dolomite, metagreywacke, quartzite and gneiss, were collected in situ using spectroradiometer and used as reference of ASTER image for the preparation of spectral signature of different lithological units. The image is applied to analysis atmospheric correction using Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) and empirical line calibration techniques to convert pixel radiance values into reflectance. A minimum noise fraction (MNF) transform is applied to identify the inherent variance of spectral reflectance and effectively discriminates various lithological units. The different types of lithological units are clearly discriminated using MNF method. Spectral Angle Mapper (SAM) classification is an effective tool for differentiating rock types and its distinct mineralogical composition from associated terrains. Spectral Angle Mapper (SAM) classification uses field-derived spectral signature to demarcate various lithological features with its spatial extent. The result shows different lithological units under Aravalli Supergroup, Banded Gneissic Complex and intrusive formations that are composed of meta-arkose, conglomerate, phyllite, mica schist, dolomite, metagreywacke and migmatites in various locations. The extracted geological features using ASTER image show strong resampling with the district resource map and validated using ground truth verification. The overall accuracy of SAM-classified map of lithological units is 73.39% and Kappa coefficient of 0.59. Mapping the lithological features using ASTER image, data coupled with MNF and SAM techniques provides relatively accurate result, and this study may be used for discrimination of lithological units with its spatial characteristics.

Species type and forest health assessment via hyperspectral remote sensing in the part of Himalayan range, India

Abstract: Ground spectra using analytical spectral device were collected for dominant species in Rajpur and Mussoorie hills of Lesser Himalayas of India for mapping dominant plant species. Hyperspectral remote sensing data (EO‐1) was used for classification of temperate species such as Quercus leucotrichophora, Cedrus deodara, Thuja orientalis, subtropical species like Pinus roxburghii, and tropical species such as Shorea robusta, Lantana camara, etc. using Spectral Angle Mapper. A total of 14 species were mapped along with the 4 other land use/cover classes (Agriculture, settlement, main river course, barren land). Map accuracy was 77% assessed on the basis of 66 ground truth ground control point. The exotic species , Lantana camara was mapped in the area which has been found to be distributed from tropical to lower temperate regions and was showing impact on the health of the neighboring species which was derived from the Hyperion data. Large impact has been observed in the Shorea robusta species and its health distribution map showed 48% healthy and 52% less healthy part. DOI: http://dx.doi.org/10.3329/dujbs.v23i2.20093 Dhaka Univ. J. Biol. Sci. 23(2): 135-146, 2014