Land-cover mapping in remote sensing (RS) applications renders rich information for
decision support and environmental monitoring systems. The derivation of such information …

An accurate identification of objects from the acquisition system depends on the clear
segmentation and classification of remote sensing images. With the limited financial …

Convolutional neural networks (CNNs) have been widely used in hyperspectral image
classification (HSIC). Although the current CNN-based methods have achieved good …

Twin support vector machine (TWSVM) has gained increasing interest from various research
fields recently. In this paper, we aim to report the current state of the theoretical research and …

Due to the rapid growth of machine learning tools and specifically deep networks in various
computer vision and image processing areas, applications of Convolutional Neural …

Support Vector Machines (SVMs) are classic supervised learning models for classification,
regression and distribution estimation. A survey conducted by Kaggle in 2017 shows that …

Following an avalanche, one of the factors that affect victims' chance of survival is the speed
with which they are located and dug out. Rescue teams use techniques like trained rescue …

This research study about image classification by using the deep neural network (DNN) or
also known as Deep Learning by using framework TensorFlow. Python is used as a …

Less training samples are a challenging problem in hyperspectral image classification.
Active learning and semisupervised learning are two promising techniques to address the …

The accurate location of clouds in images is prerequisite for many high-resolution satellite
imagery applications such as atmospheric correction, land cover classifications, and target …