Image fusion combines the images of different spectral, spatial, multi-date, as well as radiometric data to achieve a better quality image for improved classification results. Recently, Convolution Neural Network (CNN)-based classification algorithms are extensively used for remote sensing applications. Keeping this in view, present work proposes to use CNN-based fusion and classification of Sentinel 1 (VV and VH polarization) and Sentinel 2 datasets acquired over an agricultural area near Hisar (India). For image fusion, three CNN-based approaches are used to fuse Sentinel 2 (10 m) data with VV and VH bands of Sentinel 1 data. After fusion, classification was performed using 2D-CNN classifier to judge the performance of fused images in terms of classification accuracy. Results suggest that out of the three fusion approaches, only infrared image fusion (IVF) approach performed well with the considered dataset in terms of fusion indicators and classification accuracy. Keeping in view of its better performance, this study proposes a modified IVF approach by using different image pyramid methods. Comparison of results suggests an improved performance by modified IVF approach for the fusion of Sentinel 2 and Sentinel 1 data in comparison with the original IVF approach.

This study reports results of the Extreme Gradient Boosting (XGBoost) algorithm in comparison to Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) to generate cloud mask comprising of six classes: low cloud, high cloud, cloud shadow, ground, snow, and water. Here, Baetens-Hagolle dataset and WHUS2-CD dataset was considered. Various texture features derived using GLCM, morphological profile analysis, bilateral filtering, and deep features derived using Residual Network (Resnet) were used in combination with spectral information. The first phase of the study suggested both XGBoost and RF exhibit comparable performance for both traditional texture features and deep features, the second phase highlighted that XGBoost showed better generalization capabilities with respect to the different environmental conditions, and finally, comparison with threshold-based methods (Function of Mask (Fmask), Sen2cor, and Maccs-Atcor Joint Method (MAJA)) suggested that XGBoost and RF based cloud detection is a good alternative of existing state-of-the-art cloud detection.

Recently, deep learning (DL) techniques including Convolutional neural network (CNN), Recurrent neural network (RNN), and Recurrent-Convolutional neural network (R-CNN) have been extensively used to classify the remotely sensed data. Out of various deep learning algorithms, CNN-based algorithms are most widely used for the satellite image classification. Despite the improved performance of CNN, it also requires various hyper-parameters for training the network architecture to achieve the desired classification accuracy. Keeping in view the fact that the accuracy achieved by any classification algorithms is influenced by a suitable choice and value of hyper-parameter, this paper discusses the influence of several hyper-parameters on the classification accuracy of CNN classifier using three remote sensing datasets. The aim of this study is not to propose a set of values of different hyper-parameters but to study their influence on land cover classification accuracy with remote sensing datasets. Experimental results from the study indicate that various hyper-parameters affect the performance of CNN classifier to different extent suggesting a need to select the optimal value of these hyper-parameters for land cover classification studies using considered datasets.

With the continuous advancement of remote sensing technology and its growing importance, the need for ready-to-use data has increased exponentially. Satellite platforms such as Sentinel-2, which carries the Multispectral Instrument (MSI) sensor, known for their cost-effectiveness, capture valuable information about Earth in the form of images. However, they encounter a significant challenge in the form of clouds and their shadows, which hinders the data acquisition and processing for regions of interest. This article undertakes a comprehensive literature review to systematically analyze the critical cloud-related challenges. It explores the need for accurate cloud detection, reviews existing datasets, and evaluates contemporary cloud detection methodologies, including their strengths and limitations. Additionally, it highlights the inaccuracies introduced by varying atmospheric and environmental conditions, emphasizing the importance of integrating advanced techniques that can utilize local and global semantics. The review also introduces a structured intercomparison framework to enable standardized evaluation across binary and multiclass cloud detection methods using both qualitative and quantitative metrics. To facilitate fair comparison, a conversion mechanism is highlighted to harmonize outputs across methods with different class granularities. By identifying gaps in current practices and datasets, the study highlights the importance of innovative, efficient, and scalable solutions for automated cloud detection, paving the way for unbiased evaluation and improved utilization of satellite imagery across diverse applications.