R. D. Garg

Methodological Review: Health GIS and HIV/AIDS studies: Perspective and retrospective

GIS (Geographic Information System) is a useful tool that aids and assists in health research, health education, planning, monitoring and evaluation of health programmes that are meant to control and eradicate certain life threatening diseases and epidemics. HIV/AIDS is one such epidemic that poses a serious challenge and threatens the overall human welfare. This communication is an attempt to link and understand the health scenario in a GIS context with emphasis on HIV/AIDS. Various GIS based functionalities for health studies and their scope in analyzing and controlling epidemiological diseases are explored. Overall scenario of the spread of HIV/AIDS around the world is presented along with the Indian perspective. Finally, we conclude with the general management problems, issues and challenges related to HIV/AIDS prevailing in India.

Remote Sensing and GIS Based Approach for Identification of Artificial Recharge Sites

The main aim of this paper is to demonstrate the capabilities of Remote Sensing (RS) and Geographic Information System (GIS) techniques for the demarcation of suitable sites for artificial recharge of groundwater aquifers, in the Loni watershed, located in Unnao and Raebareli districts, Uttar Pradesh, India. In this study, the SCS-CN model, groundwater depth data and morphological parameters (bifurcation ratio, elongation ratio, drainage density, ruggedness number, relief ratio, and circulatory ratio) have been used to delineate the recharge sites for undertaking water conservation measures. Augmentation of water resource is proposed in the watershed by constructing runoff storage structures, like check dam, percolation tank and nala bund. The site suitability for these water harvesting structures is determined by considering spatially varying parameters, like runoff potential, slope, groundwater fluctuation data and morphometric information of the watershed. GIS has been used as an effective tool to store, analyse and integrate spatial and attribute information pertaining to runoff, slope, drainage, groundwater fluctuation and morphometric characteristics for such studies.

Automatic Road Extraction from High Resolution Satellite Image using Adaptive Global Thresholding and Morphological Operations

Road network extraction from high resolution satellite images is one of the most important aspects. In the present paper, research experimentation is carried out in order to extract the roads from the high resolution satellite image using image segmentation methods. The segmentation technique is implemented using adaptive global thresholding and morphological operations. Global thresholding segments the image to fix the boundaries. To compute the appropriate threshold values several problems are also analyzed, for instance, the illumination conditions, the different type of pavement material, the presence of objects such as vegetation, vehicles, buildings etc. Image segmentation is performed using morphological approach implemented through dilation of similar boundaries and erosion of dissimilar and irrelevant boundaries decided on the basis of pixel characteristics. The roads are clearly identifiable in the final processed image, which is obtained by superimposing the segmented image over the original enhanced image. The experimental results proved that proposed approach can be used in reliable way for automatic detection of roads from high resolution satellite image. The results can be used in automated map preparation, detection of network in trajectory planning for unmanned aerial vehicles. It also has wide applications in navigation, computer vision as a predictor-corrector algorithm for estimating the road position to simulate dynamic process of road extraction. Although an expert can label road pixels from a given satellite image but this operation is prone to errors. Therefore, an automated system is required to detect the road network in a high resolution satellite image in a robust manner.

A two-stage framework for road extraction from high-resolution satellite images by using prominent features of impervious surfaces

The segmentation and classification of high-resolution satellite images (HRSI) are useful approaches to extract information. In recent times, roads and buildings have been classified for analysis of urban areas in a better manner. Apart from these, healthy trees are also an important factor in HRSI, i.e. adjacent to roads, and vegetation. They reflect the area in an image as land cover. Other important information, shadow, is extracted from satellite images, which indicates the presence of trees and built-up areas such as buildings, flyovers, etc. In this article, a weighted membership-function-based fuzzy c-means with spatial constraints (WMFCSC) approach for automated satellite image classification is proposed. Initially, spatially fuzzy clustering is used to classify the satellite images in healthy trees with vegetation, roads, and shadows, which includes the information of spatial constraints. The road results of the classified image are still having non-road segments. Therefore, the proposed four intermediate stages (IS) are used to extract the road information, followed by the results of road areas of the WMFCSC approach. The framework of IS helps to remove the false road segments which are adjacent to roads and renovates the segmented roads due to the shadow effect. A final step of a hybrid WMFCSC-IS approach is used to extract the road network. The results of classified images confirm the effectiveness of the WMFCSC-IS approach for satellite image classification.

A HYBRID APPROACH FOR INFORMATION EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGERY

This paper presents a hybrid approach for extraction of information from high resolution satellite imagery and also demonstrates the accuracy achieved by the final extracted information. The hybrid technique comprises of improved marker-controlled watershed transforms and a nonlinear derivative method. It overcomes all the disadvantages of existing region-based and edge-based methods by incorporating aforesaid hybrid methods. It preserves the advantages of multi-resolution and multi-scale gradient approaches. Region-based segmentation also incorporates the watershed technique due to its better efficiency in segmentation. In principle, a proper segmentation can be performed perfectly by watershed technique on incorporating ridges. These ridges express as the objects boundaries according to the property of contour detection. On the other hand, the nonlinear derivative method is used for resolving the discrete edge detection problem. Since it automatically selects the best edge localization, which is very much useful for estimation of gradient selection. The main benefit of univocal edge localization is to provide a better direction estimation of the gradient, which helps in producing a confident edge reference map for synthetic images. The practical merit of this proposed method is to derive an impervious surface from emerging urban areas.

Regression-Kriging Technique to Downscale Satellite-Derived Land Surface Temperature in Heterogeneous Agricultural Landscape

The study introduces regression-kriging technique to downscale land surface temperature (LST) over a heterogeneous landscape of India and compares it with other models. All models are initially tested on aggregated 960 m Landsat LST and downscaled to 240 m (RMSE = 0.45°C) and 120 m (RMSE = 0.68°C) resolution. Finally, the MODIS LST is downscaled to 250 m (RMSE = 0.7°C) resolution. The proposed model outperformed compared to other models. The qualitative assessment reveals appearance of box-like structure is not seen in the proposed model-derived LST due to reconstruction of residual.

Pre-processing of TerraSAR-X Data for Speckle Removal: An Approach for Performance Evaluation

Measures to select despeckling filter to remove speckles from TerraSAR-X image have been evaluated for an area having varied land cover classes. Gamma, Lee, Sigma, Mean, Median and Frost filters were applied in different window sizes. These images are visually compared and four parameters viz. Noise Mean Value (NMV), Noise Variance (NV), Mean Square Difference (MSD) and Equivalent Numbers of Looks (ENL) are calculated. These parameters were well implemented to compare results of various despeckling filters on TerraSAR-X image recorded in high resolution spot mode and checked for the constraints. Median 7x7 filter was found most suitable among other filters to despeckle the images.

Fixed Point ICA Based Approach for Maximizing the Non-gaussianity in Remote Sensing Image Classification

To maximize the non-gaussianity in sphered satellite data, many authors have proposed different independent component analysis (ICA) based approaches to classify images by reducing the mixing effect in classes. In multispectral data, few heterogeneous classes have little variation in spectral resolution. Even though, a classified image should exhibit high spectral variance among different classes, while it should be less within a particular class. To improve the classification accuracy in the presence of mixed classes i.e., having similar spectral characteristics, a novel method improved fixed point independent component analysis (IFPICA) is proposed. This method segregates the objects from mixed classes on maximizing the approximation of negentropy, which reduces the effect of quite similar spectral characteristics among different classes. It can easily estimate the independent component of this non-gaussian distribution of data with the help of nonlinearity. Therefore, this nonlinearity helps to optimize the performance of this approach, which minimizes the variance among similar classes. Due to the presence of neural algorithms, it is quite robust, computationally simple and has very fast convergence, in respect to the spectral distributions of satellite images. Hence, this proposed IFPICA approach plays a major role in the classification of satellite images such as road, vegetation, buildings and grassland area. The images used in the study doesn’t have any initial or additive noise, which would obstruct the process of IFPICA algorithm used in the work, therefore preprocessing is not required for noise suppression in this work. The post-processing, e.g., deflation, denoising, filtering, etc. are also not required due to similar reason.

Classification of high resolution satellite images using spatial constraints-based fuzzy clustering

Abstract A spatial constraints-based fuzzy clustering technique is introduced in the paper and the target application is classification of high resolution multispectral satellite images. This fuzzy-C-means (FCM) technique enhances the classification results with the help of a weighted membership function ( W mf ). Initially, spatial fuzzy clustering (FC) is used to segment the targeted vegetation areas with the surrounding low vegetation areas, which include the information of spatial constraints (SCs). The performance of the FCM image segmentation is subject to appropriate initialization of W mf and SC. It is able to evolve directly from the initial segmentation by spatial fuzzy clustering. The controlling parameters in fuzziness of the FCM approach, W mf and SC, help to estimate the segmented road results, then the Stentiford thinning algorithm is used to estimate the road network from the classified results. Such improvements facilitate FCM method manipulation and lead to segmentation that is more robust. The results confirm its effectiveness for satellite image classification, which extracts useful information in suburban and urban areas. The proposed approach, spatial constraint-based fuzzy clustering with a weighted membership function ( SCFCW mf ), has been used to extract the information of healthy trees with vegetation and shadows showing elevated features in satellite images. The performance values of quality assessment parameters show a good degree of accuracy for segmented roads using the proposed hybrid SCFCW mf -MO (morphological operations) approach which also occluded nonroad parts.

Performance analysis of different predictive models for crop classification across an aridic to ustic area of Indian states

Abstract The purpose of this study is to present comparative performance analysis of different machine learning algorithms for large area crop classification. Ten Indian districts with significant rabi crops viz. wheat, mustard, gram, red lentils (masoor) have been selected for the study. Most popular classical ensemble models – bagging/ARCing, random forest (RF), gradient boosting and Importance Sampled Learning Ensemble (ISLE) with traditional single model (decision tree) have been selected for comparative analysis. To incorporate dependency of large area crop in different variables viz. parent material and soil, phenology, texture, topography, soil moisture, vegetation, climate etc., 35 digital layers are prepared using different satellite data (ALOS DEM, Landsat-8, MODIS NDVI, RISAT-1, Sentinental-1A) and climatic data (precipitation, temperature). In rabi season, field survey about crop type is carried out to prepare training data. Performance is evaluated on the basis of marginal rates, F-measure and Jaccard’s coefficient of community, Classification Success Index and Agreement Coefficients. Score is calculated to rank the algorithm. RF is best performer followed by gradient boosting for crop classification. Other ensemble methods ARCing, bagging and ISLE are in decreasing order of performance. Traditional non-ensemble method decision tree scored higher than ISLE.