An informativeness of the data has always been of great interest within a machine learning community. Nowadays, with a skyrocketing advancement of the artificial intelligence and massive volumes of noisy data, it becomes even more essential to develop a robust and effective methods for training data optimization. Existing approaches are mostly based on empirical trials and errors, with either stochastic or deterministic data reduction strategies. The key limitation of such solutions is that they do not consider the overall informativeness of the resulting training dataset. In this paper, we propose a novel approach for a quality aware dataset optimization by initial assessment of its informativeness. As a metric of informativeness, we use entropy values calculated over the target dataset. To alleviate the computational complexity, we perform initial clustering of the dataset into many subsets and calculate the entropy of each cluster independently. The dataset is then optimized by dynamic programming to find a sequence of subsets with low overall entropy according to imposed size limitations. The experimental evaluation shows the that proposed approach shows improvement over current best alternatives in terms of accuracy, precision, recall and F1-score metrics. Moreover, the proposed approach provides an excellent interclass discrimination even for a large number of classes.

Continuous technological growth and the corresponding environmental implications are triggering the enhancement of advanced environmental monitoring solutions, such as remote sensing. In this paper, we propose a new method for the spatial point patterns generation by classifying remote sensing images using convolutional neural network. To increase the accuracy, the training samples are extended by the suggested data augmentation scheme based on the similarities of images within the same part of the landscape for a limited observation time. The image patches are classified in accordance with the labels of previously classified images of the manually prepared training and test samples. This approach has improved the accuracy of image classification by 7% compared to current best practices of data augmentation. A set of image patch centers of a particular class is considered as a random point configuration, while the class labels are used as marks for every point. A marked point pattern is regarded as a combination of several subpoint patterns with the same qualitative marks. We analyze the bivariate point pattern to identify the relationships between points of different types using the features of a marked random point pattern.

Accurate boundary detection between distinct classes is a major challenge in the semantic segmentation of remote sensing images. A widely used strategy involves dividing large images into patches for classification, which requires carefully prepared training, test, and validation datasets. When the resolution of the input image exceeds that of the available labels, upsampling the labels to match the image resolution often leads to suboptimal results. To address this issue, we propose an alternative approach, Adaptive Patch Reshaping (APR), that subdivides both the input image and label patches, then reassigns them to classes according to an information-theoretic criterion. By reshaping patches based on their similarity to representative class characteristics, this method constructs a new training set from the original one. Experimental results using Sentinel-2 imagery demonstrate that the proposed technique, implemented with a lightweight CNN, achieved a classification precision of 89% for the training set, 86% for the test set, and 81% for the validation set. Furthermore, comparative analysis confirms that APR improves the delineation of boundaries between regions, yielding higher F1-scores and Structural Similarity Index values compared to standard patch-based baselines.