Anil Kumar

Bio: Anil Kumar is an academic researcher from Indian Institute of Remote Sensing. The author has contributed to research in topics: Fuzzy logic & Contextual image classification. The author has an hindex of 10, co-authored 98 publications receiving 501 citations. Previous affiliations of Anil Kumar include National Institute of Technology, Rourkela & Indian Space Research Organisation.

The N-glycan processing enzymes α-mannosidase and β-D-N-acetylhexosaminidase are involved in ripening-associated softening in the non-climacteric fruits of capsicum

Abstract: Excessive softening of fruits during the ripening process leads to deterioration. This is of significant global importance as softening-mediated deterioration leads to huge postharvest losses. N-glycan processing enzymes are reported to play an important role during climacteric fruit softening: however, to date these enzymes have not been characterized in non-climacteric fruit. Two ripening-specific N-glycan processing enzymes, α-mannosidase (α-Man) and β-D-N-acetylhexosaminidase (β-Hex), have been identified and targeted to enhance the shelf life in non-climacteric fruits such as capsicum (Capsicum annuum). The purification, cloning, and functional characterization of α-Man and β-Hex from capsicum, which belong to glycosyl hydrolase (GH) families 38 and 20, respectively, are described here. α-Man and β-Hex are cell wall glycoproteins that are able to cleave terminal α-mannose and β-D-N-acetylglucosamine residues of N-glycans, respectively. α-Man and β-Hex transcripts as well as enzyme activity increase with the ripening and/or softening of capsicum. The function of α-Man and β-Hex in capsicum softening is investigated through RNA interference (RNAi) in fruits. α-Man and β-Hex RNAi fruits were approximately two times firmer compared with the control and fruit deterioration was delayed by approximately 7 d. It is shown that silencing of α-Man and β-Hex enhances fruit shelf life due to the reduced degradation of N-glycoproteins which resulted in delayed softening. Altogether, the results provide evidence for the involvement of N-glycan processing in non-climacteric fruit softening. In conclusion, genetic engineering of N-glycan processing can be a common strategy in both climacteric and non-climacteric species to reduce the post-harvest crop losses.

Effect on specific crop mapping using WorldView-2 multispectral add-on bands: soft classification approach

Abstract: In this study, new add-on bands in a multispectral dataset of WorldView-2, DigitalGlobe's second next-generation satellite, have been evaluated. For extraction of a specific agriculture crop at a time, WorldView-2 multispectral single, as well as two-date data sets, were used. For this purpose, a class-based sensor independent spectral band ratio normalized difference vegetation index (NDVI) (CBSI-NDVI) and its possibilistice fuzzy classification approach was used. Different agriculture crops selected for the study were sugarcane, late wheat, cauliflower, berseem (fodder), early wheat and ratoon. It is found that bands four and eight with temporal data are good for extracting sugarcane, while bands four, eight and five, seven with temporal data are suitable for late wheat and bands four and eight work well for cauliflower. Similarly, bands five, seven and five, eight with temporal data are good for extracting berseem (fodder), bands four, eight work for early wheat with temporal data and for ratoon four, six single date or four, six and four, eight temporal data. This suitability of bands has been observed with respect to a maximum membership value difference, as well as maximum entropy difference, between the two closest agriculture crops. Thus, it can be concluded that existing bands five, seven and new bands four, six, eight in WorldView-2 are important for identifying and mapping crops mentioned in this study. This indicates new bands, especially four, six, eight introduced in WorldView-2, are more effective than existing bands in QuickBird for mapping specific crops.

The Indian-French Trishna Mission: Earth Observation in the Thermal Infrared with High Spatio-Temporal Resolution

Abstract: The monitoring of the water cycle at the Earth surface which tightly interacts with the climate change processes as well as a number of practical applications (agriculture, soil and water quality assessment, irrigation and water resource management, etc…) requires surface temperature measurements at local scale. Such is the goal of the Indian-French high spatio-temporal TRISHNA mission (Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment). The scientific objectives of the mission and research work conducted to consolidate the mission specifications are presented. Progress in modelling of surface fluxes is then discussed. The main specifications of the mission such as the revisit, the spatial resolution, the overpass time, the spectral bands and the orbit are analyzed and justified. The resulting baseline of the mission is given.

Full fuzzy land cover mapping using remote sensing data based on fuzzy c-means and density estimation

Abstract: The three stages in supervised digital classification of remote sensing data are training, classification, and testing. The commonly adopted approaches assume that boundaries between classes are crisp and hard classification is applied. In the real world, however, as spatial resolution decreases significantly, the proportion of mixed pixels increases. This leads to vagueness or fuzziness in the data, and in such situations researchers have applied the fuzzy approach at the classification stage. Some researchers have tried fuzzy approaches at the training, classification, and testing stages (full fuzzy concept) using statistical and artificial neural network methods. In this paper a full fuzzy concept has been presented, at a subpixel level, using density estimation using support vector machine (D-SVM) and fuzzy c-means (FCM) approaches. These approaches (SVM and FCM) were evaluated with respect to a fuzzy weighted matrix. In this test study using a four-channel dataset, a comparison of methods has found t...

ALCM: Automatic land cover mapping

Abstract: It may be quite important for resource management people to extract single land cover class, at sub-pixel level from multi-spectral remote sensing images of different areas in single step processing. It has been observed, that neural network can be trained to extract single land cover class from multi-spectral remote sensing images, but they have problems in setting various parameters and slow during training stage. This paper present single land cover class water, extraction from mixed pixels present in multiple multi-spectral remote sensing data sets of same bands of AWiFS sensor of Resoursesat-1 (IRS-P6) satellite from different areas. In this work fuzzy logic-based algorithm, which is independent of statistical distribution assumption of data, has been studied at sub-pixel level to handle mixed pixels. It has been found; possibilistic c-means (PCM) algorithm takes the possibilistic view, that the membership of a feature vector in a class has nothing to do with its membership in other classes. Due to this, it was observed that PCM can extract only one class, from remote sensing multi-spectral data and it has produced 93.7% and 97.1% overall sub-pixel classification accuracy for two different data sets of different places using LISS-III (IRS-P6) reference data of same dates as of AWiFS data.

Support vector machines in remote sensing: A review

Abstract: A wide range of methods for analysis of airborne- and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.

Remote Sensing And Image Interpretation

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Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site. [on Mars]

Abstract: A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.