Proceedings of 2nd International Conference on Communication, Computing and Networking, 2018
This paper outlines the intents to develop and assess remotely sensed spectral responses of Vigna... more This paper outlines the intents to develop and assess remotely sensed spectral responses of Vigna radiata crop with an adaxial surface positioned at Aurangabad region by Latitude 19.897827 and Longitude 75.308666. Current exploration will be useful for crop surge monitoring centered on spectral features collected using ASD Field Spec 4 spectroradiometer intended for the estimation of photosynthetic pigments. The proposed approach resides preprocessing techniques followed via postprocessing methods instigated by python open source software. The preprocessing was prepared using parabolic correction technique with (.asd) files format. Spectral features were projected for detection of photosynthetic pigments using ten categories of indices. Among the diverse phonological patterns of crop progression, the respectable aggregate of a coefficient of correlations was found with fluctuating growth parameters at jointing phase of Vigna radiata. The spectral vegetation indices (SVI) desired for the investigation were composed at jointing and ripening crop phases. SVI was given superior outcomes with R2 values speckled between 0.91 and 0.99, in addition, good amount of correlation was observed in between NDVI and PSSR-a. The NDVI index was found to be the appropriate parameter for healthy crops and ARI2 was found appropriate for detection of disease crops. Multiple regression equations were used by means of stepwise regression technique using open source software.
With the advent of technological era, conversion of scanned document (handwritten or printed) int... more With the advent of technological era, conversion of scanned document (handwritten or printed) into machine editable format has attracted many researchers. This paper deals with the problem of recognition of Gujarati handwritten numerals. Gujarati numeral recognition requires performing some specific steps as a part of preprocessing. For preprocessing digitization, segmentation, normalization and thinning are done with considering that the image have almost no noise. Further affine invariant moments based model is used for feature extraction and finally Support Vector Machine (SVM) and Fuzzy classifiers are used for numeral classification.. The comparison of SVM and Fuzzy classifier is made and it can be seen that SVM procured better results as compared to Fuzzy Classifier.
Processing and Analysis of Hyperspectral Data, Jan 22, 2020
Hyperspectral data processing and analysis mainly plays a vital role in detection, identification... more Hyperspectral data processing and analysis mainly plays a vital role in detection, identification, discrimination and estimation of earth surface materials. It involves atmospheric correction, dimensionality reduction, endmember extraction, spectral unmixing and classification phases. One of the ultimate aims of hyperspectral data processing and analysis is to achieve high classification accuracy. The classification accuracy of hyperspectral data most probably depends upon image-derived endmembers. Ideally, an endmember is defined as a spectrally unique, idealized and pure signature of a surface material. Extraction of consistent and desired endmember is one of the important criteria to achieve the high accuracy of hyperspectral data classification and spectral unmixing. Several methods, strategies and algorithms are proposed by various researchers to extract the endmembers from hyperspectral imagery. Most of these techniques and algorithms are significantly dependent on user-defined input parameters, and this issue is subjective because there is no standard specificity about these input parameters. This leads to inconsistencies in overall endmember extraction. To resolve the aforementioned problems, systematic, generic, robust and automated mechanism of endmember extraction is required. This chapter gives and highlights the generic approach of endmember extraction with popular algorithm limitations and challenges.
In this paper we propose the Support Vector Machine (SVM) based recognition scheme towards the re... more In this paper we propose the Support Vector Machine (SVM) based recognition scheme towards the recognition of Gujarati handwritten numerals. The preprocessing is done considering morphological operations. For computing the features, each isolated numeral is segmented into blocks. These blocks create base for four sets of features. Then we derived affine invariant moments as features. The features obtained from these blocks are fed to SVM classifier. We obtained the recognition rate of 91% approximately.
This paper highlights the study regarding the classification of crop types using the techniques b... more This paper highlights the study regarding the classification of crop types using the techniques based on Gray Level Co-occurrence Matrix (GLCM) and support vector machine (SVM). The dataset used was from IRS-LISS IV sensor with 5.8 m spatial resolution having three spectral bands of date 4-October 2014 for our chosen location at 20°07 13.5 N 75°23 05.3 E. Classification of all three bands followed by classification of GLCM measures (of all three bands) was accomplished by using Support Vector Machine classifier with Radial Basis Function. The accuracy of classification obtained from GLCM was 90.29% with the Kappa coefficient
Communications in Computer and Information Science, 2018
The main focus of this study is to measure accuracy of classification for combined spectral featu... more The main focus of this study is to measure accuracy of classification for combined spectral features and textural features of fused images obtained after applying different fusion techniques. Study area is selected with a variety of land cover features so as to understand effect of fusion on different land cover features. IHS, GS, PC, CN and Brovey fusion methods are used. A different layer stacked method has been used to create a composite image of bands 5, 4 and 3 of Landsat8, since they show maximum spectral reflectance variations in the land features present in the selected area of study. Resampling is carried out using Nearest Neighbor and Cubic Convolution, where Nearest Neighbor gave better accuracy of fused image. Classification is performed using spectral features of fused images, textural features of fused images and composite images created using spectral and textural features. Fused images are assessed for distortion using visual analysis and statistical parameters. Classification accuracy is measured using error matrix and it has observed that Neural Net classifier produced better accuracies than other classifiers like Maximum Likelihood, Minimum distance classifier and Spectral Angle Mapper. Land cover and land use (LULC) classification accuracy is enhanced using textural features of fused images. Keywords: Accuracy Á Classification Á Error matrix Á Image fusion Land cover and land use Á Spectral features Á Textural features
Communications in Computer and Information Science, 2019
Traditionally, the earth surface features were collected by field visit for collecting the detail... more Traditionally, the earth surface features were collected by field visit for collecting the details of earth surface features with coordinates. However, the conventional method is slow tedious and costly. Recently, smartphone applications are providing the details of locations including latitude, longitude which is basically used for ground truth data collection for Remote Sensing (RS) applications. Nevertheless, there are limitations found in existing system as they do not provide offline and online mode, location data management, and visualization into the single system. The objective of our research was thus to overcome the limitations in the existing ground truth collection system by exploring a novel solution that will be based on Smartphone, Cloud server, and WebGIS platform. Consequently, an android application and web-based tool is developed for ground truth data collection and its management. The present research reports, the novel solution for collecting the real-time ground truth and mapping feature for RS research.
2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), 2017
The objective of this paper is to report the study carried out to assess and evaluate changes in ... more The objective of this paper is to report the study carried out to assess and evaluate changes in Land-Use Land-Cover(LULC) at the region of Aurangabad Municipal Corporation (AMC) for the year 2009 and 2015 using multispectral images acquired from remotely sensed Linear-Imaging-Self-Scanning-Sensor-HI(LISS-HI). The area was categorized into six types, viz. Residential(R), Vegetation(V), Water_Body(W), Rock(Ro), Barren Land(B) and Fallow_Land(F). Four different types of supervised classifiers have been used and it was found the Maximum Likelihood classifier has provided satisfactory and reliable results. The overall accuracy with the classifier was found to be 83% and 93% with Kappa Coefficient 0.78 and 0.90 for the year 2009 and 2015, respectively. The residential area was found to be increased by 1.35% whereas area related to Water Body, Vegetation and Fallow Land have decreased by 0.83%, 2.59% and 18.43% respectively. The areas for Rock remain same, as it was reserved. The area cov...
Biometric security is challenging task in day to day life because it is difficult to avoid the fr... more Biometric security is challenging task in day to day life because it is difficult to avoid the fraud. In this research paper emerging biometric trait, i.e. Hyper Spectral Face is considered for human authentication system. There are various visible spectrum of electromagnetic spectral bands are considered for face recognition instead of only three RGB bands. Hyper Spectral gives band wise more finite detail information of face. It is very novel and more accurate than ordinary face recognition system. Hong Kong PolyU Hyper Spectral Face Database used for Face recognition. Kernel Principle Component Analysis (KPCA) algorithm gives prominent features of the Hyperspectral Face Dataset. Gabor + KPCA + Mahalabonis distance on the verification rate at 1% FAR on the evaluation set 89.74%.
An identification of plant species is essential for many applications such as plant species class... more An identification of plant species is essential for many applications such as plant species classification along with its current status and yield estimation. In the present paper, we have investigated the analysis of chlorophyll content of three different types of plants with healthy & dry leaves (Synygium Cumini, Adhatola Zeylanica and Azadirachta Indica) using values of the spectral indices. The variations Chlorophyll a, Chlorophyll b and Carotenoid content of healthy and dry leaves of plant based on spectral indices were determined. The statistical analysis has been done through open source software based on python. The hyperspectral data was acquired of plants healthy and dry using ASD devices. The computed healthy leaves values of the pigment specific simple ratio (PSSRc) were found to be the highest value i.e. (5.200) and corresponding value for dry leaves was (3.810). It was possible to estimate content of chlorophyll in plants using the hyperspectral data.
Proceedings of 2nd International Conference on Communication, Computing and Networking, 2018
This paper outlines the intents to develop and assess remotely sensed spectral responses of Vigna... more This paper outlines the intents to develop and assess remotely sensed spectral responses of Vigna radiata crop with an adaxial surface positioned at Aurangabad region by Latitude 19.897827 and Longitude 75.308666. Current exploration will be useful for crop surge monitoring centered on spectral features collected using ASD Field Spec 4 spectroradiometer intended for the estimation of photosynthetic pigments. The proposed approach resides preprocessing techniques followed via postprocessing methods instigated by python open source software. The preprocessing was prepared using parabolic correction technique with (.asd) files format. Spectral features were projected for detection of photosynthetic pigments using ten categories of indices. Among the diverse phonological patterns of crop progression, the respectable aggregate of a coefficient of correlations was found with fluctuating growth parameters at jointing phase of Vigna radiata. The spectral vegetation indices (SVI) desired for the investigation were composed at jointing and ripening crop phases. SVI was given superior outcomes with R2 values speckled between 0.91 and 0.99, in addition, good amount of correlation was observed in between NDVI and PSSR-a. The NDVI index was found to be the appropriate parameter for healthy crops and ARI2 was found appropriate for detection of disease crops. Multiple regression equations were used by means of stepwise regression technique using open source software.
With the advent of technological era, conversion of scanned document (handwritten or printed) int... more With the advent of technological era, conversion of scanned document (handwritten or printed) into machine editable format has attracted many researchers. This paper deals with the problem of recognition of Gujarati handwritten numerals. Gujarati numeral recognition requires performing some specific steps as a part of preprocessing. For preprocessing digitization, segmentation, normalization and thinning are done with considering that the image have almost no noise. Further affine invariant moments based model is used for feature extraction and finally Support Vector Machine (SVM) and Fuzzy classifiers are used for numeral classification.. The comparison of SVM and Fuzzy classifier is made and it can be seen that SVM procured better results as compared to Fuzzy Classifier.
Processing and Analysis of Hyperspectral Data, Jan 22, 2020
Hyperspectral data processing and analysis mainly plays a vital role in detection, identification... more Hyperspectral data processing and analysis mainly plays a vital role in detection, identification, discrimination and estimation of earth surface materials. It involves atmospheric correction, dimensionality reduction, endmember extraction, spectral unmixing and classification phases. One of the ultimate aims of hyperspectral data processing and analysis is to achieve high classification accuracy. The classification accuracy of hyperspectral data most probably depends upon image-derived endmembers. Ideally, an endmember is defined as a spectrally unique, idealized and pure signature of a surface material. Extraction of consistent and desired endmember is one of the important criteria to achieve the high accuracy of hyperspectral data classification and spectral unmixing. Several methods, strategies and algorithms are proposed by various researchers to extract the endmembers from hyperspectral imagery. Most of these techniques and algorithms are significantly dependent on user-defined input parameters, and this issue is subjective because there is no standard specificity about these input parameters. This leads to inconsistencies in overall endmember extraction. To resolve the aforementioned problems, systematic, generic, robust and automated mechanism of endmember extraction is required. This chapter gives and highlights the generic approach of endmember extraction with popular algorithm limitations and challenges.
In this paper we propose the Support Vector Machine (SVM) based recognition scheme towards the re... more In this paper we propose the Support Vector Machine (SVM) based recognition scheme towards the recognition of Gujarati handwritten numerals. The preprocessing is done considering morphological operations. For computing the features, each isolated numeral is segmented into blocks. These blocks create base for four sets of features. Then we derived affine invariant moments as features. The features obtained from these blocks are fed to SVM classifier. We obtained the recognition rate of 91% approximately.
This paper highlights the study regarding the classification of crop types using the techniques b... more This paper highlights the study regarding the classification of crop types using the techniques based on Gray Level Co-occurrence Matrix (GLCM) and support vector machine (SVM). The dataset used was from IRS-LISS IV sensor with 5.8 m spatial resolution having three spectral bands of date 4-October 2014 for our chosen location at 20°07 13.5 N 75°23 05.3 E. Classification of all three bands followed by classification of GLCM measures (of all three bands) was accomplished by using Support Vector Machine classifier with Radial Basis Function. The accuracy of classification obtained from GLCM was 90.29% with the Kappa coefficient
Communications in Computer and Information Science, 2018
The main focus of this study is to measure accuracy of classification for combined spectral featu... more The main focus of this study is to measure accuracy of classification for combined spectral features and textural features of fused images obtained after applying different fusion techniques. Study area is selected with a variety of land cover features so as to understand effect of fusion on different land cover features. IHS, GS, PC, CN and Brovey fusion methods are used. A different layer stacked method has been used to create a composite image of bands 5, 4 and 3 of Landsat8, since they show maximum spectral reflectance variations in the land features present in the selected area of study. Resampling is carried out using Nearest Neighbor and Cubic Convolution, where Nearest Neighbor gave better accuracy of fused image. Classification is performed using spectral features of fused images, textural features of fused images and composite images created using spectral and textural features. Fused images are assessed for distortion using visual analysis and statistical parameters. Classification accuracy is measured using error matrix and it has observed that Neural Net classifier produced better accuracies than other classifiers like Maximum Likelihood, Minimum distance classifier and Spectral Angle Mapper. Land cover and land use (LULC) classification accuracy is enhanced using textural features of fused images. Keywords: Accuracy Á Classification Á Error matrix Á Image fusion Land cover and land use Á Spectral features Á Textural features
Communications in Computer and Information Science, 2019
Traditionally, the earth surface features were collected by field visit for collecting the detail... more Traditionally, the earth surface features were collected by field visit for collecting the details of earth surface features with coordinates. However, the conventional method is slow tedious and costly. Recently, smartphone applications are providing the details of locations including latitude, longitude which is basically used for ground truth data collection for Remote Sensing (RS) applications. Nevertheless, there are limitations found in existing system as they do not provide offline and online mode, location data management, and visualization into the single system. The objective of our research was thus to overcome the limitations in the existing ground truth collection system by exploring a novel solution that will be based on Smartphone, Cloud server, and WebGIS platform. Consequently, an android application and web-based tool is developed for ground truth data collection and its management. The present research reports, the novel solution for collecting the real-time ground truth and mapping feature for RS research.
2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), 2017
The objective of this paper is to report the study carried out to assess and evaluate changes in ... more The objective of this paper is to report the study carried out to assess and evaluate changes in Land-Use Land-Cover(LULC) at the region of Aurangabad Municipal Corporation (AMC) for the year 2009 and 2015 using multispectral images acquired from remotely sensed Linear-Imaging-Self-Scanning-Sensor-HI(LISS-HI). The area was categorized into six types, viz. Residential(R), Vegetation(V), Water_Body(W), Rock(Ro), Barren Land(B) and Fallow_Land(F). Four different types of supervised classifiers have been used and it was found the Maximum Likelihood classifier has provided satisfactory and reliable results. The overall accuracy with the classifier was found to be 83% and 93% with Kappa Coefficient 0.78 and 0.90 for the year 2009 and 2015, respectively. The residential area was found to be increased by 1.35% whereas area related to Water Body, Vegetation and Fallow Land have decreased by 0.83%, 2.59% and 18.43% respectively. The areas for Rock remain same, as it was reserved. The area cov...
Biometric security is challenging task in day to day life because it is difficult to avoid the fr... more Biometric security is challenging task in day to day life because it is difficult to avoid the fraud. In this research paper emerging biometric trait, i.e. Hyper Spectral Face is considered for human authentication system. There are various visible spectrum of electromagnetic spectral bands are considered for face recognition instead of only three RGB bands. Hyper Spectral gives band wise more finite detail information of face. It is very novel and more accurate than ordinary face recognition system. Hong Kong PolyU Hyper Spectral Face Database used for Face recognition. Kernel Principle Component Analysis (KPCA) algorithm gives prominent features of the Hyperspectral Face Dataset. Gabor + KPCA + Mahalabonis distance on the verification rate at 1% FAR on the evaluation set 89.74%.
An identification of plant species is essential for many applications such as plant species class... more An identification of plant species is essential for many applications such as plant species classification along with its current status and yield estimation. In the present paper, we have investigated the analysis of chlorophyll content of three different types of plants with healthy & dry leaves (Synygium Cumini, Adhatola Zeylanica and Azadirachta Indica) using values of the spectral indices. The variations Chlorophyll a, Chlorophyll b and Carotenoid content of healthy and dry leaves of plant based on spectral indices were determined. The statistical analysis has been done through open source software based on python. The hyperspectral data was acquired of plants healthy and dry using ASD devices. The computed healthy leaves values of the pigment specific simple ratio (PSSRc) were found to be the highest value i.e. (5.200) and corresponding value for dry leaves was (3.810). It was possible to estimate content of chlorophyll in plants using the hyperspectral data.
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