This paper addresses the problem of the classification of hyperspectral remote sensing
images by support vector machines (SVMs). First, we propose a theoretical discussion and …

The aim of this paper is twofold. First, we present a thorough experimental study to show the
superiority of the generalization capability of the support vector machine (SVM) approach in …

Recent remote sensing literature has shown that support vector machine (SVM) methods
generally outperform traditional statistical and neural methods in classification problems …

Agriculture is an important pillar of world development, and smart agriculture is an emerging
paradigm in current practices. RGB Images contain rich information and play an increasingly …

We propose the use of support vector machines (SVMs) for automatic hyperspectral data
classification and knowledge discovery. In the first stage of the study, we use SVMs for crop …

Video processing systems such as HEVC requiring low energy consumption needed for the
multimedia market has lead to extensive development in fast algorithms for the efficient …

The compression of hyperspectral images (HSIs) has recently become a very attractive issue
for remote sensing applications because of their volumetric data. In this paper, an efficient …

Video data has become the largest source of data consumed globally. Due to the rapid
growth of video applications and boosting demands for higher quality video services, video …

We study the use of machine learning for visual quality evaluation with comprehensive
singular value decomposition (SVD)-based visual features. In this paper, the two-stage …

This paper proposes a new framework for digital image processing; it relies on inexact
computing to address some of the challenges associated with the discrete cosine transform …