S. Sanjeevi

Landslide susceptibility mapping in a hilly terrain using remote sensing and GIS

Remote sensing and Geographic Information System (GIS) are well suited to landslide studies. The aim of this study is to prepare a landslide susceptibility map of a part of Ooty region, Tamil Nadu, India, where landslides are common. The area of the coverage is approximately 10 × 14 km in a hilly region where planting tea, vegetables and cash crops are in practice. Hence, deforestation, formation of new settlements and changing land use practices are always in progress. Land use and land cover maps are prepared from Indian Remote Sensing Satellite (IRS 1C - LISS III) imagery. Digital Elevation Model (DEM) was developed using 20 m interval contours, available in the topographic map. Field studies such as local enquiry, land use verification, landslide location identification were carried out. Analysis was carried out with GIS software by assigning rank and weights for each input data. The output shows the possible landslide areas, which are grouped for preparation of landslide susceptibility maps.

Automatic detection of ridges in lunar images using phase symmetry and phase congruency

Lunar surface exploration is increasing rapidly. These exploring satellites provide a large number of high resolution images containing topographical information. The topographical information in lunar surface are craters, ridges, mountains and grabens. Extracting this topographical information manually is time-consuming. Hence, an automatic feature extraction is favored. This paper presents a novel approach using image processing techniques to automatically detect ridges in lunar images. The approaches adopted for this development includes phase symmetry, phase congruency and morphological operations to automatically detect significant ridges. The phase symmetry extracts symmetry features with discontinuities, phase congruency extracts features lying in low contrast regions and morphological operations such as thinning and pruning are used to obtain significant ridges. The proposed novel approach experiments on a test set of different regions. These different region images are obtained from different sensors (LROC, Selene and Clementine) having different spatial resolution and illumination variation. The results obtained are compared with the plan curvature method; and they are evaluated based on true and false detection of ridge pixels. Irrespective of illumination variation and spatial resolution, the proposed approach provides better results than the plan curvature method and its detection rate is approximately 92%. We have proposed a novel approach for automatic ridge detection in lunar images using phase symmetry and phase congruency method.All ridge detection techniques are available only for terrestrial data. This is the first attempt of automatically identifying ridges in lunar data.The automatic detection approach has been attempted on different resolution of lunar images.We examine the symmetric nature of the ridges in lunar images using the phase symmetry component.The ridges are extracted automatically using image processing techniques.

Spectral unmixing ofcompact airborne spectrographic imager (CASI) data for quantifying sub-pixel proportions of biophysical parameters in a coastal dune system

In environmentally sensitive and large coastal dune systems, identification and mapping of favourable (sandy), and unfavourable (scrub rich) habitats form the key to coastal conservation and management. In highly mixed floral environments, however, such an identification is difficult with low resolution multispectral imagery. In such cases, spectral unmixing is useful to resolve the “mixed pixel” effects. The Kenfig NNR (National Nature Reserve), South Wales. UK, bestowed with high biodiversity, is facing loss of successionally young slack habitats due to dune stabilisation and vegetation succession. To map such habitats, linear spectral unmixing of airborne MSS (CASI-Compact Airborne Spectrographic Imager) data was performed using the Constrained Least Square (CLS) method, and the sub-pixel proportions of the spectral end members viz. sand, vegetation and shade/moisture were defined. Comparison of the estimated fractions with the growth forms of the dominant vegetation species Salix repens (Creeping willow) with classified digital aerial photographs shows a positive correlation, thus giving us confidence in the mixture modelling technique. Apart from aiding in conservation management, such a fuzzy classification of multi-date imagery helps to delineate sandy and vegetated areas for change detection and landscape/ habitat succession studies.

Spectral studies of lunar equivalent rocks — A prelude to lunar mineral mapping

Hyperspectral remote sensing technique is widely applied for geological studies including the study of extra-terrestrial rocks. Since it has many spectral bands, discrimination between rocks and minerals can be done more precisely. To perform chemical and mineralogical mapping and to study the rocks on the lunar surface, India has proposed to launch its first lunar remote sensing satellite Chandrayaan-1 in the year 2008. For mineralogical mapping, the mission will carry a Hyperspectral Imager (HySI) instrument, which operates in the VNIR region. This paper presents-an attempt to study the spectral response of lunar-akin terrestrial rocks, in the VNIR region (as in the case of the proposed HySI on-board Chandrayaan-1). For this purpose, rocks similar to those present on the lunar surface were collected and their spectral response in the 64 simulated bands of HySI sensor were studied using a spectro-radiometer. Petrographic studies and modal analysis were carried out using thin sections of the rock samples. On studying the spectral response of the lunar-like rock samples in the 64 HySI bands, it is seen that there are distinct absorption features in bands 58 (923.75nm-927.5nm) and 63 (942.5nm-946.25nm) of the NIR wavelength ranges, for basalt rocks; distinct reflectance features in band 20 (590nm to 600nm) for ganmbbro: distinct reflectance features in band 19 (580nm to 590nm) and absorption in band 18 (570-580nm) for gabbroic anorthosite and distinct reflection features in band 63 (942.5nm to 946.25nm) for anorthosite. Thus, this study demonstrates the possibility of identifying the minerals and rocks on lunar surface using the hyperspectral approach and the spectral signatures of lunar-like rocks present on Earth.

Effectiveness of soft classification approaches as inputs for super resolution mapping of Hyperion image: a study on Peechi Reservoir, south India

Image classification has evolved from per-pixel to sub-pixel and from sub-pixel to super resolution mapping approaches. Super-resolution mapping (SRM) is a technique which allows mapping at the sub-pixel scale. Super-resolution mapping proves to be the better approach for the accurate classification of coarse spatial resolution images and to resolve mixed pixels in the boundary of such images. The accuracy of the super-resolved output depends on the input derived from the soft classification technique. This paper aims to compare the potential of support vector machine (SVM), spectral angle mapper (SAM) and linear spectral unmixing (LSU) as inputs for super-resolution mapping. The fraction image, distance measure image and probability image obtained from linear spectral unmixing, spectral angle mapper and support vector machine respectively are used as an input for super resolution mapping designed on Hopfield Neural Network (HNN) for the Hyperion image of Peechi reservoir, south India. Effectiveness of the inputs is evaluated by estimating the water-spread area of the Peechi reservoir from each of the outputs. The results indicate that the accuracy of any super-resolution approach depends on the inputs from the soft classification approaches. The accuracy of the water spread area estimated from the classified outputs is 95.9%, 96.6% and 99.7% from LSU, SAM and SVM respectively as inputs for the SRM method. Thus, the HNN based SRM method proves to be better when the soft classification input is from SVM.Image classification has evolved from per-pixel to sub-pixel and from sub-pixel to super resolution mapping approaches. Super-resolution mapping (SRM) is a technique which allows mapping at the sub-pixel scale. Super-resolution mapping proves to be the better approach for the accurate classification of coarse spatial resolution images and to resolve mixed pixels in the boundary of such images. The accuracy of the super-resolved output depends on the input derived from the soft classification technique. This paper aims to compare the potential of support vector machine (SVM), spectral angle mapper (SAM) and linear spectral unmixing (LSU) as inputs for super-resolution mapping. The fraction image, distance measure image and probability image obtained from linear spectral unmixing, spectral angle mapper and support vector machine respectively are used as an input for super resolution mapping designed on Hopfield Neural Network (HNN) for the Hyperion image of Peechi reservoir, south India. Effectiveness of the inputs is evaluated by estimating the waterspread area of the Peechi reservoir from each of the outputs. The results indicate that the accuracy of any superresolution approach depends on the inputs from the soft classification approaches. The accuracy of the water spread area estimated from the classified outputs is 95.9%, 96.6% and 99.7% from LSU, SAM and SVM respectively as inputs for the SRM method. Thus, the HNN based SRM method proves to be better when the soft classification input is from SVM.