… Given that at training time we have only access to image classification labels, we need a
way to aggregate these pixel-level scores into a single image-level classification score sk = …

… pixel-level classifiers, eg, random forests [3], [4], neural networks [5], [6], and active learning
[7], [8]. Generally, pixel-level classification … spectral pixels in HSI, the pixel-level methods are …

… Image fusion at pixel level requires a higher resolution panchromatic band and a lower
resolution multispectral image to be executed. In this study, four popular pixel level image fusion …

… of the image. This paper describes methods of image segmentation by pixel classification
ina two-dimensional feature space, where the features are the gray level and the edge …

… Semantic image segmentation is a conventional approach that provides pixel-level
image classification. Since the first deep learning-based method invented by Long et al. (2015), …

… Since Random Forests is a soft classifier, it also allows for a quantification of the classification
probabilities at the pixel level. Each pixel is attributed a probability distribution, with the …

… -level image fusion is recognized as having high significance in a variety of fields including
… image processing problems. The application of DL techniques in the field of pixel-level image …

… spatial correlation among pixels in HSI and avoid the “salt-and-pepper” problem generated
in the pixel-based HSI classification, a novel pixellevel and superpixel-level aware subspace …

… with only image-level labels for fully convolutional network … We evaluate the classification
and pixel-level localization … knowledge transfer between the image classification task and the …

… This review provides a survey of various pixel-level image … Due to this advantage, pixel-level
image fusion has shown … of the state of the art pixel-level image fusion methods. Then, the …