Deforestation remains a critical global environmental concern, requiring effective monitoring approaches. This letter presents a novel attention-powered encoder–decoder neural network designed to address the key challenges in deforestation mapping, including scale heterogeneity, temporal dynamics, and computational efficiency. The proposed framework integrates a modified YOLOv8 backbone, spatial attention (SA) mechanisms, and a conjugated Dice–Focal loss function to enhance sensitivity to small- and large-scale deforestation patterns in temporal remote sensing (RS) data. An extensive battery of tests was conducted using two datasets from the Amazon region, exploring both single-image and image-pair inputs under varying contextual and class balance conditions. The results attest to substantial improvements in accuracy and computational efficiency compared to 13 deep learning (DL) methods, establishing the proposed model as effective in deforestation monitoring scenarios, where accuracy, scalability, and computational cost are simultaneously critical.

Polarimetric Synthetic Aperture Radar (PolSAR) sensors have emerged as a groundbreaking remote sensing technology. They enable the acquisition of the amplitude, phase, and orientation of electromagnetic waves across multiple polarizations. This capability provides enhanced potential for detailed environmental analysis. However, challenges such as complex data structures, non-Gaussian noise properties, and low signal-to-noise ratios pose significant barriers to the effective use of PolSAR data. Existing methods for modeling and analyzing PolSAR data are predominantly parametric and rely on assumptions that may fail under certain conditions. Aware of these limitations, this study introduces the use ofU-statistics for PolSAR data analysis. Using information from the diagonal intensities of the covariance matrix, we propose a hypothesis testing mechanism to assess sample homogeneity a top-down hierarchical separability analysis, and aU-statistics-based classification approach. The proposed procedures are validated using an ALOS PALSAR image of the Amazonian region. The results show the robustness and effectiveness of the proposed methods, offering a reliable framework for analyzing and classifying PolSAR data under non-parametric assumptions.

Change detection is a fundamental task that involves assessing changes in a given region over multiple time periods. It has been widely applied across various fields, including monitoring deforestation, urban expansion, and natural disaster analysis. In this article, we address the critical and complex issue of automatically identifying types of changes in land cover using remotely sensed imagery. While conventional unsupervised change detection methods typically focus on comparing pairs of images and making a binary decision between “change” and “nonchange,” our approach tackles the challenge of analyzing long image series and identifying the kind of change. Under this condition, the unsupervised change detection process allows for a more informative identification of the land cover dynamics. Moreover, our approach transforms input data to a new representation, capturing the target’s spectral response changes over time. Through the utilization of stochastic distances and an optimized thresholding scheme, areas exhibiting minimal spectral response variance are classified as unchanged, effectively distinguishing them from regions undergoing modifications. Next, by applying autocorrelation analysis, regions exhibiting temporal modifications are segregated into periodic (i.e., seasonal) and aperiodic (i.e., permanent) change cases. Experimental validation using both simulated and real-world remote sensing image series demonstrates the effectiveness of the proposed approach.

Deep Learning (DL) has become one of the key approaches for dealing with many challenges in medical imaging, which includes lung segmentation in Computed Tomography (CT). The use of seeded segmentation methods is another effective approach to get accurate partitions from complex CT images, as they give users autonomy, flexibility and easy usability when selecting specific targets for measurement purposes or pharmaceutical interventions. In this paper, we combine the accuracy of deep contour leaning with the versatility of seeded segmentation to yield a semi-automatic framework for segmenting lung CT images from patients affected by COVID-19. More specifically, we design a DL-driven approach that learns label diffusion maps from a contour detection network integrated with a label propagation model, used to diffuse the seeds over the CT images. Moreover, the trained model induces the diffusion of the seeds by only taking as input a marked CT-scan, segmenting hundreds of CT slices in an unsupervised and recursive way. Another important trait of our framework is that it is capable of segmenting lung structures even in the lack of well-defined boundaries and regardless of the level of COVID-19 infection. The accuracy and effectiveness of our learned diffusion model are attested to by both qualitative as well as quantitative comparisons involving several user-steered segmentations methods and eight CT data sets containing different types of lesions caused by COVID-19.

Classifying targets in satellite images is a nontrivial task which requires dealing with a large number of undesirable elements such as clouds, building shadows and other unexpected objects. Among these, a commonly found element refers to artificially inserted post-processing objects like textual content, as the added text usually takes the form of watermarks, sensor specifications, street and place location names, etc. Manually selecting text segments is tedious, time-consuming, and requires the familiarity with image editing tools to precisely delineate these writing areas. Therefore, in this paper, a new automatic approach for detecting textual elements in satellite images is presented. Our approach combines cartoon-texture decomposition, thresholding-based rules, morphological operations, and connected component analysis into a fully automated and concise framework. Experiments on real satellite images and comparisons against well-established text detection methods demonstrate the high accuracy and low false-positive rate achieved by our approach when detecting textual content.

Change detection is a topic of great interest in remote sensing. A good similarity metric to compute the variations among the images is the key to high-quality change detection. However, most existing approaches rely on the fixed threshold values or the user-provided ground truth in order to be effective. The inability to deal with artificial objects such as clouds and shadows is a significant difficulty for many change-detection methods. We propose a new unsupervised change-detection framework to address those critical points. The notion of homogeneous regions is introduced together with a set of geometric operations and statistic-based criteria to characterize and distinguish formally the change and nonchange areas in a pair of remote sensing images. Moreover, a robust and statistically well-posed family of stochastic distances is also proposed, which allows comparing the probability distributions of different regions/objects in the images. These stochastic measures are then used to train a support-vector-machine-based approach in order to detect the change/nonchange areas. Three study cases using the images acquired with different sensors are given in order to compare the proposed method with other well-known unsupervised methods.

Kernel functions have revolutionized theory and practice in the field of pattern recognition, especially to perform image classification. Besides giving rise to nonlinear variants of the well-known support vector machine (SVM), these functions have also been successfully used to classify nonvectorial data (e.g., graphs and collection of sets), in which customized metrics are created to precisely measure the similarity among such contextual data entities. This letter introduces two context-inspired kernel functions as new SVM-driven methods for remote sensing image classification. In contrast to the existing SVM-based approaches that assume only multiattribute vectors as representative features in a high-dimensional space, the proposed models formally establish comparisons between the entire sets of context-given data, thus employing these contextual measurements to drive the classification. More precisely, stochastic distances as well as hypothesis tests are conveniently handled and “kernelized” to build our models. A complete battery of experiments involving both remote sensing and real-world images is conducted to validate the performance of the proposed kernels against various well-established SVM-based methods.

Support Vector Machine (SVM) is a method widely used for image classification. The original formulation of this method does not incorporate contextual information. This study brings a new perspective regarding contextual SVM. The main idea of the presented proposal consists on translates, individually for each pixel using it contextual information, the separation hyperplane originally designed by SVM. A case study using ALOS PALSAR image shows that the proposed method produces better results than traditional SVM.

Region based methods are indicated to classify image with strong heterogeneity, where only the spectral information is not enough. Different approaches have been proposed to perform this kind of classification. This study presents a new approach for region based classification that consists in use the Support Vector Machine (SVM) method with Bhattacharyya kernel function. A high resolution IKONOS image was classified. The classification results shows that SVM method using the Bhattacharyya kernel is better than Minimum Distance Classifier and conventional SVM.

Supervised and unsupervised learning are two well disseminated and discussed paradigms which define how image classification techniques extract knowledge about the data. A recent learning paradigm, called semi-supervised, comes to solve some limitations of supervised learning, as the amount of information needed to conduce an appropriated learning process. Different models of semi-supervised learning have been proposed in literature, which ones basically explore statistical or clustering data proprieties. This work presents a simulation study on the performance of some semi-supervised learning models, applied in image classification methods.