The building extraction from synthetic aperture radar (SAR) images has always been a
challenging research topic. Recently, the deep convolution neural network brings excellent …

Environmental damage has been of much concern, particularly in coastal areas and the
oceans, given climate change and the drastic effects of pollution and extreme climate …

Remote sensing data play a crucial role in monitoring crop dynamics in the context of
precision agriculture by characterizing the spatial and temporal variability of crop traits. At …

Land use/Land cover (LU/LC) change analysis is the present-day challenging task for the
researchers in defining the environmental change across the world in the field of remote …

Clustering is a popular technique that can help make large datasets more manageable and
usable by grou** together similar objects. Most clustering approaches are too …

In this paper, computer vision technology is used to quickly and accurately identify and
classify the surface defects of processed bamboo, which overcomes the low efficiency of …

The remote sensing technology provides the means of classification of land cover with
diversity of additional endless environmental variables over large spatial and moderate …

Abstract Support Vector Machine (SVM) is one of the most effective machine learning
algorithms widely employed for classification tasks. SVMs perform well in high-dimensional …

Deep learning-based methods, especially convolutional neural networks (CNNs), have
shown their effectiveness for image classification. In this paper, vision transformer …

Polarimetric SAR (PolSAR) image classification is an increasing area of research in the field
of remote sensing and computer vision. It is mainly used for land cover classification, which …