Image semantic segmentation using deep learning algorithms plays a vital role in identifying different rock-forming minerals. In this paper, we employ the U-net model for its architecture that guarantees precise localization and efficient data utilization. We implement this deep learning model across two distinct datasets: (1) the first dataset from the ALEX Streckeisen website, and (2) the second dataset from the Gabal Nikeiba area, South Eastern Desert of Egypt. Our model exhibits excellent performance in both datasets, with an average accuracy of precision at 0.89 and 0.83, recall at 0.80 and 0.78, and F1 score at 0.82 and 0.79, respectively, helping in identifying and detecting rock-forming minerals in thin-section images. The model’s most exceptional performance is clearly in eleven different basement rock-forming minerals with precision up to 0.89, recall at 0.80, and F1 score at 0.82 on average. This study is significant as it represents the key to identifying and detecting minerals in the thin sections of rock samples in Egypt and the Arabian–Nubian Shield as a whole. By significantly reducing analysis time and improving accuracy compared to manual methods, it revolutionizes geological research and resource exploration in the region.

The deep learning neural network is a recent development that has become the subject of research in the computer vision and remote sensing disciplines. Super resolution (SR) images can be obtained using deep neural network methods that achieve a higher performance than all previous traditional methods. Here, in this study, the objective is to describe existing deep learning methods for SR satellite images. Different satellite data are used to predict the performance of each deep learning model. This article presents a brief overview of most deep learning techniques and compares them to obtain a more effective and efficient model. The deep network cascade model outperforms other deep learning algorithms; this algorithm is dependable in the reconstruction process for obtaining SR images and overcomes some drawbacks found in traditional reconstruction algorithms. The sparse coding network method remains valuable, and with some enhancements, further improvement in results can be achieved.

Super resolution (SR) image reconstruction refers to a process of generating a high resolution image from several low resolution images. There is a high demand for highresolution satellite sensing in modern applications. SR offers an affordable solution for this high demand. The accuracy of super resolution depends on the accuracy of determining the difference between the low-resolution images. The widespread use of superresolution methods, in a variety of applications such as remote sensing has led to an increasing need for or quality assessment measures. Assessment for image quality traditionally needs its original image as a reference. The traditional method for assessment like Peak Signal to Noise Ratio (PSNR) or Mean Square Error (MSE) difficult when there is no reference. This paper is focused on No-Reference (NR) quality measures for SR images using blur and sharpness (CPBD, LPC-SI). A non-reference objective measure is proposed, which aims to evaluate the quality of the super-resolution satellite images that are constructed without the need for a full reference condition and the result will be reliable. This article presents an overview assessment of SR techniques and measuring the quality of the image. We illustrate shift estimation which is the first and the most critical step in super resolution process. Then several super resolution reconstruction techniques have been discussed and compared. Satellite images (SPOT-5) and other Remote Sensing (RS) data are used in the experiment. The images have sub pixel shifts 0.5 in the horizontal and vertical directions respectively.

Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model's success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a wide range of modern applications. However, there are some challenges related to the availability of annotated data, the complex nature of data, and model parameterization, which strongly impact performance. In this article, a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures (vintage Convolutional Neural Network, CNN; Fully Convolutional Networks, FCN; encoder-decoder, recurrent networks; attention models, and generative adversarial models). The characteristics, capabilities, and limitations of current DL models were examined, and potential research directions were discussed.