Estimation of Aboveground Phytomass of Plantations Using Digital Photogrammetry and High Resolution Remote Sensing Data

The study follows an approach to estimate phytomass using recent techniques of remote sensing and digital photogrammetry. It involved tree inventory of forest plantations in Bhakra forest range of Nainital district. Panchromatic stereo dataset of Cartosat-1 was evaluated for mean stand height retrieval. Texture analysis and tree-tops detection analyses were done on Quick-Bird PAN data. The composite texture image of mean, variance and contrast with a 5x5 pixel window was found best to separate tree crowns for assessment of crown areas. Tree tops count obtained by local maxima filtering was found to be 83.4 % efficient with an RMSE+/-13 for 35 sample plots. The predicted phytomass ranged from 27.01 to 35.08 t/ha in the case of Eucalyptus sp. while in the case of Tectona grandis from 26.52 to 156 t/ha. The correlation between observed and predicted phytomass in Eucalyptus sp. was 0.468 with an RMSE of 5.12. However, the phytomass predicted in Tectona grandis was fairly strong with R-2=0.65 and RMSE of 9.89 as there was no undergrowth and the crowns were clearly visible. Results of the study show the potential of Cartosat-1 derived DSM and Quick-Bird texture image for the estimation of stand height, stem diameter, tree count and phytomass of important timber species.

Automatic Road Extraction using High Resolution Satellite Image Based on Texture Progressive Analysis and Normalized Cut Method

In this paper the approach for automatic road extraction for an urban region using structural, spectral and geometric characteristics of roads has been presented. Roads have been extracted based on two levels: Pre-processing and road extraction methods. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, parking lots, vegetation regions and other open spaces). The road segments are then extracted using Texture Progressive Analysis (TPA) and Normalized cut algorithm. The TPA technique uses binary segmentation based on three levels of texture statistical evaluation to extract road segments where as, Normalizedcut method for road extraction is a graph based method that generates optimal partition of road segments. The performance evaluation (quality measures) for road extraction using TPA and normalized cut method is compared. Thus the experimental result show that normalized cut method is efficient in extracting road segments in urban region from high resolution satellite image.

Integration of speckle de-noising and image segmentation using synthetic aperture radar image for flood extent extraction

Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Study of texture evolution in metastable beta-Ti alloy as a function of strain path and its effect on alpha transformation texture

Texture evolution in a low cost beta titanium alloy was studied for different modes of rolling and heat treatments. The alloy was cold rolled by unidirectional and multi-step cross rolling. The cold rolled material was either aged directly or recrystallized and then aged. The evolution of texture in alpha and beta phases were studied. The rolling texture of beta phase that is characterized by the gamma fiber is stronger for MSCR than UDR; while the trend is reversed on recrystallization. The mode of rolling affects alpha transformation texture on aging with smaller alpha lath size and stronger alpha texture in UDR than in MSCR. The defect structure in beta phase influences the evolution of a texture on aging. A stronger defect structure in beta phase leads to variant selection with the rolled samples showing fewer variants than the recrystallized samples.

Crop Classification using Biologically-inspired Techniques with High Resolution Satellite Image

Remote sensing provides a lucid and effective means for crop coverage identification. Crop coverage identification is a very important technique, as it provides vital information on the type and extent of crop cultivated in a particular area. This information has immense potential in the planning for further cultivation activities and for optimal usage of the available fertile land. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Further, image classification forms the core of the solution to the crop coverage identification problem. No single classifier can prove to satisfactorily classify all the basic crop cover mapping problems of a cultivated region. We present in this paper the experimental results of multiple classification techniques for the problem of crop cover mapping of a cultivated region. A detailed comparison of the algorithms inspired by social behaviour of insects and conventional statistical method for crop classification is presented in this paper. These include the Maximum Likelihood Classifier (MLC), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) techniques. The high resolution satellite image has been used for the experiments.

Solution conformations of penta and heptapeptides containing repetitive α-aminoisobutyryl-Image -alanyl and α -aminoisobutyryl-Image -valyl sequences

The presence of folded solution conformations in the peptides Boc-Ala-(Aib-Ala)2-OMe, Boc-Val-(Aib-Val) 2-OMe, Boc-Ala-(Aib-Ala)3-OMe and Boc-Val-(Aib-Val)3-OMe has been established by 270MHz 1H NMR. Intramolecularly H-bonded NH groups have been identified using temperature and solvent dependence of NH chemical shifts and paramagnetic radical induced broadening of NH resonances. Both pentapeptides adopt 310 helical conformations possessing 3 intramolecular H-bonds in CDCl3 and (CD3)2SO. The heptapeptides favour helical structures with 5 H-bonds in CDCl3. In (CD3)2SO only 4 H-bonds are readily detected.

Contribution of texture to the strengthening and fracture in hot-rolled magnesium-12.7 at % cadmium alloy

The grain size dependencies of the yield and fracture stresses in hot rolled Mg-12.7 at % Cd alloy have been measured in the temperature range 77 to 420 K and are found to be in accordance with HalI-Petch type of equations. In hot rolled Mg-12.7 Cd alloy, the HalI-Petch intercept a w is higher than that in hot rolled magnesium, while the slope ky is comparable. The fracture is intercrystalline at 77 K, mixed mode at 300 K and ductile at 420 K. The above flow and fracture behaviours are interpreted in terms of the complimentary effects of texture hardening and solid solution strengthening.

Evolution of crystallographic texture and microstructure in the orthorhombic phase of a two-phase alloy Ti-22Al-25Nb

Evolution of crystallographic texture in the orthorhombic phase of a two-phase alloy Ti–22Al–25Nb (at%), consisting of orthorhombic (O) and bcc (β/B2) phases, was studied. The material was subjected to deformation in two-phase field as well as in the single β phase field. The resulting evolution of microstructure and crystallographic texture were recorded using scanning electron microscopy and X-ray diffraction. The orthorhombic phase underwent change in morphology (from platelets to equiaxed) on rolling in the two-phase field with the texture getting sharper with the amount of deformation. Rolling above β transus temperature led to hot deformation of single β phase microstructure and its subsequent cooling produced transformed coarse platelets of orthorhombic phase with texture in orientation relation with the high temperature deformed β phase.

Image reconstruction with laterally truncated projections in helical cone-beam CT: Linear prediction based projection completion techniques

Lateral or transaxial truncation of cone-beam data can occur either due to the field of view limitation of the scanning apparatus or iregion-of-interest tomography. In this paper, we Suggest two new methods to handle lateral truncation in helical scan CT. It is seen that reconstruction with laterally truncated projection data, assuming it to be complete, gives severe artifacts which even penetrates into the field of view. A row-by-row data completion approach using linear prediction is introduced for helical scan truncated data. An extension of this technique known as windowed linear prediction approach is introduced. Efficacy of the two techniques are shown using simulation with standard phantoms. A quantitative image quality measure of the resulting reconstructed images are used to evaluate the performance of the proposed methods against an extension of a standard existing technique.

Simultaneous display of spectrum and image in a single plane, and the realization of a lensless coherent optical processor

A novel method, designated the holographic spectrum reconstruction (HSR) method, is proposed for achieving simultaneous display of the spectrum and image of an object in a single plane. A study of the scaling behaviour of both the spectrum and the image has been carried out and based on this study, it is demonstrated that a lensless coherent optical processor can be realized.

Texture strengthening in warm-rolled cadmium-1% silver alloy

The variation in the tensile properties at 77 K and 300 K in warm-rolled (300 K) Cd-1% Ag alloy with deformation has been studied in longitudinal as well as transverse specimens. The low-temperature yield strength increases with warm rolling without much loss in ductility. The strength at 300 K, however, decreases with heavy warm deformation. From microstructural studies and X-ray investigations, it was observed that changes in grain size and texture occur during warm rolling. Both these changes are found to be important in deciding the tensile properties. The longitudinal and transverse strengths at 77 K vary linearly with l-frac12, where l is the average grain diameter, and thus they obey the Hall-Petch relation. The Hall-Petch slope, k, is lower in specimens with favourable lcub1013rcub texture while the intercept σo is higher when the lcub0002rcub texture is less favourable.

Enzyme-catalysed non-oxidative decarboxylation of aromatic acids: I.Purification and spectroscopic properties of 2,3 dihydroxybenzoic acid decarboxylase from Image Image

In order to understand the molecular mechanism of non-oxidative decarboxylation of aromatic acids observed in microbial systems, 2,3 dihydroxybenzoic acid (DHBA) decarboxylase from Image Image was purified to homogeneity by affinity chromatography. The enzyme (Mr 120 kDa) had four identical subunits (28 kDa each) and was specific for DHBA. It had a pH optimum of 5.2 and Km was 0.34mM. The decarboxylation did not require any cofactors, nor did the enzyme had any pyruvoyl group at the active site. The carboxyl group and hydroxyl group in the Image -position were required for activity. The preliminary spectroscopic properties of the enzyme are also reported.