This research work aims to study the effect of training parameter concept and sample size in
the process of classification by using a fuzzy Possibilistic c-Means (PCM) approach for …

For specific crop map**, temporal data is used to handle the spectral overlap between
classes. But a number of times, availability of temporal optical remote sensing data is not …

Conventional Noise Clustering (NC) algorithms do not consider any spatial information in
the image. In this study, three algorithms have been presented, Noise Local Information c …

In remote sensing images, isolated pixels in the form of salt-and-pepper noisy pixels
deteriorates the image classification results. To handle these noisy pixels, spatial constraints …

This study explores the application of fuzzy soft classification techniques combined with
vegetation indices to address spectral overlap and heterogeneity in agricultural image …

The quality of training data in terms of precision is instrumental in delivering a good
performance for a supervised or a semi-supervised machine learning algorithm. The role of …

The use of spectral indices is prevalent in remote sensing data processing for a variety of
applications. However, the spectral bands considered to formulate conventional spectral …

Remote sensing is widely used in agriculture to monitor crop health, map harvested areas,
and estimate soil properties like moisture, texture, and fertility. High temporal resolution …

The ability to identify changes on the ground using multi-temporal earth observation data is
one of the main issues in remote sensing. Map** of species and identification of changes …

The fastest growth in population directly impacts the urbanization process leading to
changes in the landscape and thus necessitates the study of land use/land cover. Deep …