Segment based classification of Indian urban environment

This paper presents results of segment based classification of an Indian urban environment. This approach to classification involved three stages. In the first stage, a region based multispectral segmentation of the image was carried out after determining suitable automatic threshold values considering textured nature of imagery. The second stage involved refinement of initially segmented image, iteratively by merging smaller segments with the most similar adjacent segments until they satisfied a homogeneity criterion. Finally, these segments were classified into 12 different classes using various spectral and textural properties of segments. Three different types of classifications were performed: the per-pixel Gaussian maximum likelihood classification (GMLC), per-segment GML classification, and the per-segment neural classification. Result showed that per-segment classification improves overall classification accuracy by more than 25% in comparison to per-pixel approach

An experimental Indian gravimetric geoid model using Curtin University’s approach

Over the past decade, numerous advantages of a gravimetric geoid model and its possible suitability for the Indian national vertical datum have been discussed and advocated by the Indian scientific community and national geodetic agencies. However, despite several regional efforts, a state-of-the-art gravimetric geoid model for the whole of India remains elusive due to a multitude of reasons. India encompasses one of the most diverse topographies on the planet, which includes the Gangetic plains, the Himalayas, the Thar desert, and a long peninsular coastline, among other topographic features. In the present study, we have developed the first national geoid and quasigeoid models for India using Curtin University’s approach. Terrain corrections were found to reach an extreme of 187 mGal, Faye gravity anomalies 617 mGal, and the geoid-quasigeoid separation 4.002 m. We have computed both geoid and quasigeoid models to analyse their representativeness of the Indian normal-orthometric heights from the 119 GNSS-levelling points that are available to us. A geoid model for India has been computed with an overall standard deviation of ±0.396 m but varying from ±0.03 m to ±0.158 m in four test regions with GNSS-levelling data. The greatest challenge in developing a precise gravimetric geoid for the whole of India is data availability and its preparation. More densely surveyed precise gravity data and a larger number of GNSS/levelling data are required to further improve the models and their testing

Conjoint analysis for quantification of relative importance of various factors affecting BPANN classification of urban environment

This paper is an attempt to suggest an approach for eliminating the lengthy process of selecting various factors while using Backpropagation artificial neural network (BPANN) and to quantify the relative importance of the factors affecting classification results. A novel approach called conjoint analysis has been used here. The paper also presents the classification results of an Indian urban environment using two BPANN approaches and compares them with conventional Gaussian Maximum Likelihood (GML) classification approach. The study showed that conjoint analysis can be successfully used to select various parameters of BPANN prior to carrying out the classifications using any of the BPANN approach. Factors like size of training samples and first hidden layer come out as some of the most important factors while the second hidden layer has the least affect on classification accuracy. Resilient backpropagation method of BPANN is the best and robust method for urban classification. Results also showed that classification obtained using BPANN approach were similar or numerically better than GML classification though the difference was not statistically significantly different

Per-field classification of Indian urban environment using IRS-1c satellite data

The paper presents investigations to determine the suitability of conventional per-pixel approach and results of per-field (segment) classification for classifying Indian urban environment using high spatial resolution satellite data. Three different types of classifications were performed: the per-pixel classification, per-field GML classification and the per-field neural classification. Result showed that per-field classification improves overall classification accuracy up to 25% in comparison to per-pixel approach

Combined radiometer and scatterometer derived soil moisture product for the Indo-Gangetic basin

This work comprises combining the passive radiometer (SMAP) and active scatterometer (ASCAT) remotely sensed surface soil moisture datasets by employing cumulative distributive frequency matching algorithm using second-order polynomial regression for the Indo-Gangetic basin by keeping GLDAS-NOAH as the reference dataset for the period 2015–2016. In order to evaluate the quality, utility and applicability of the combined soil moisture product for a macro river basin, it is further downscaled to 1 km spatial resolution using the universal triangle algorithm. The acceptable ranges of correlation coefficients (0.6–0.75 and 0.5–0.75 between derived soil moisture product with precipitation and the ground soil moisture data, respectively) indicate an interdependency between the surface soil moisture and precipitation and validate the data product too. The results have also shown satisfactory correlation coefficient and RMSE in the range 0.7–0.85 between derived downscaled product and the active-passive soil moisture product, SMAP/Sentinel-1

Neuro-textural classification of Indian urban environment

Experiments were conducted to see the effects of a set of factors on the Resilient backpropagation (Rprop) artificial neural network classification of an Indian urban environment using IRS-1C satellite data. Factors investigated were sample size, number of neurons in hidden layers and number of epochs. The effect of including texture information in the form of neighbourhood information and grey level co-occurance matrix (GLCM) features in the classification process has been explored. Statistically similar overall classification accuracy is achieved for Rprop and Gaussian maximum likelihood classification (GMLC). Investigations have revealed that a large sample size gave higher test accuracy; variation in number of neurons in hidden layer did not affect the overall classification accuracy significantly; lesser number of epochs resulted in higher overall test accuracy. Incorporation of texture information by both approaches improved classification accuracy in a statistically significant manner