Urban forests globally face severe degradation due to human activities and natural disasters, making deforestation an urgent environmental challenge. Remote sensing technology and very-high-resolution (VHR) bitemporal satellite imagery enable change detection (CD) for monitoring forest changes. However, deep learning techniques for forest CD concatenate bitemporal images into a single input, limiting the extraction of informative deep features from individual raw images. Furthermore, they are developed for middle to low-resolution images focused on specific forests such as the Amazon or a single element in the urban environment. Therefore, in this study, we propose deep learning-based urban forest CD along with overall changes in the urban environment by using VHR bitemporal images. Two networks are used independently: DeepLabv3+ for generating binary forest cover masks, and a deeply supervised image fusion network (DSIFN) for the generation of a binary change mask. The results are concatenated for semantic CD focusing on forest cover changes. To carry out the experiments, full scene tests were performed using the VHR bitemporal imagery of three urban cities acquired via three different satellites. The findings reveal significant changes in forest covers alongside urban environmental changes. Based on the accuracy assessment, the networks used in the proposed study achieved the highest F1-score, kappa, IoU, and accuracy values compared with those using other techniques. This study contributes to monitoring the impacts of climate change, rapid urbanization, and natural disasters on urban environments especially urban forests, as well as relations between changes in urban environment and urban forests.

Recently, an increase in wildfire incidents has caused significant damage from economical, humanitarian, and environmental perspectives. Wildfires have increased in severity, frequency, and duration because of climate change and rising global temperatures, resulting in the release of massive volumes of greenhouse gases, the destruction of forests and associated habitats, and the damage to infrastructures. Therefore, identifying burned areas is crucial for monitoring wildfire damage. In this study, we aim at detecting forest burned areas occurring in South Korea using optical satellite images. To exploit the advantage of applying machine learning, the present study employs representative three machine learning methods, Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and U-Net, to detect forest burned areas with a combination of input variables, namely Surface Reflectance (SR), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR). Two study sites of recently occurred forest fire events in South Korea were selected, and Sentinel-2 satellite images were used by considering a small scale of the forest fires. The quantitative and qualitative evaluations according to the machine learning methods and input variables were carried out. In terms of the comparison focusing on machine learning models, the U-Net showed the highest accuracy in both sites amongst the designed variants. The pre and post fire images by SR, NDVI, NBR, and difference of indices as the main inputs showed the best result. We also demonstrated that diverse landcovers may result in a poor burned area detection performance by comparing the results of the two sites.

Multitemporal very-high-resolution (VHR) satellite images are used as core data in the field of remote sensing because they express the topography and features of the region of interest in detail. However, geometric misalignment and radiometric dissimilarity occur when acquiring multitemporal VHR satellite images owing to external environmental factors, and these errors cause various inaccuracies, thereby hindering the effective use of multitemporal VHR satellite images. Such errors can be minimized by applying preprocessing methods such as image registration and relative radiometric normalization (RRN). However, as the data used in image registration and RRN differ, data consistency and computational efficiency are impaired, particularly when processing large amounts of data, such as a large volume of multitemporal VHR satellite images. To resolve these issues, we proposed an integrated preprocessing method by extracting pseudo-invariant features (PIFs), used for RRN, based on the conjugate points (CPs) extracted for image registration. To this end, the image registration was performed using CPs extracted using the speeded-up robust feature algorithm. Then, PIFs were extracted based on the CPs by removing vegetation areas followed by application of the region growing algorithm. Experiments were conducted on two sites constructed under different acquisition conditions to confirm the robustness of the proposed method. Various analyses based on visual and quantitative evaluation of the experimental results were performed from geometric and radiometric perspectives. The results evidence the successful integration of the image registration and RRN preprocessing steps by achieving a reasonable and stable performance.

Image registration is the most basic preprocessing method used to unify coordinates among multitemporal very-high-resolution (VHR) satellite images, thus allowing the acquisition of reliable data of the Earth’s surface. Although image registration requires multiple matching points (MP), false matching points (FMP) are included because of the similar spectral patterns and noise. However, removing FMPs from VHR satellite image pairs is challenging, especially when the images are directly affected by complex factors, such as shadow, relief displacement, and terrain shielding. Therefore, we propose a false matching point removal network (FMPR-net) based on deep learning to eliminate effectively the FMPs to improve registration accuracy. The training dataset is produced by a semi-automatic method. It involves the generation of image patch pairs based on a matching process of scale-invariant feature transform and the assignment of labels referring to the characteristics of true matching points (TMP) and FMPs. The FMPR-net is designed in a Siamese format consisting of two matching point deep feature extractors (MDFE). The architecture of the MDFE consists of one main network and three branch networks to achieve robust extraction of meaningful deep features describing the characteristics of MPs. The FMPR-net removes the FMPs using a true matching probability calculated based on the similarity between deep features. Experiments conducted on four pairs of VHR satellite images have demonstrated that the FMPR-net can effectively remove the FMPs. Consequently, accurate VHR satellite image registration is possible by reducing uncertainty caused by the FMPs.

Vegetation segmentation in Very High-Resolution (VHR) satellite imagery has become an essential task for ecological monitoring, supporting diverse applications such as large-scale vegetation conservation and detailed segmentation of small local areas. In particular, multi-class vegetation segmentation, which distinguishes various vegetation types beyond simple binary segmentation of vegetation and non-vegetation, enables detailed analysis of subtle ecosystem changes and has gained increasing importance. However, the annotation of VHR satellite imagery requires extensive time and effort, resulting in a lack of datasets for vegetation segmentation, especially those including multi-class annotations. To address this limitation, this study proposes MultiVeg, a deep learning dataset based on VHR satellite imagery for detailed multi-class vegetation segmentation. MultiVeg includes preprocessed 0.5 m resolution images collected by the KOMPSAT-3 and 3A satellites from 2014 to 2023, covering diverse environments such as urban, agricultural, and forest regions. Each image was carefully annotated by experts into three semantic classes, which are Background, Tree, and Low Vegetation, and validated through a structured quality check process. To verify the effectiveness of MultiVeg, seven representative semantic segmentation models, including convolutional neural network and Transformer-based architectures, were trained and comparatively analyzed. The results demonstrated consistent segmentation performance across all classes, confirming that MultiVeg is a high-quality and reliable dataset for deep learning-based multi-class vegetation segmentation research using VHR satellite imagery. The MultiVeg will be publicly available through GitHub (release v1.0), serving as a valuable resource for advancing deep leaning-based vegetation segmentation research in the remote sensing field.

Synthetic Aperture Radar (SAR) imagery enables high-resolution observation regardless of weather conditions. However, it is difficult to interpret intuitively due to issues such as speckle noise. In contrast, optical imagery provides realistic visual information but is vulnerable to weather and illumination changes. Leveraging this complementarity, SAR-to-Optical image translation has attracted considerable attention. Nevertheless, existing paired learning approaches are limited by the high cost of large-scale data collection and residual misregistration errors, while unpaired learning approaches are prone to global color shifts and structural distortions. To address these limitations, this study proposes a SAR-to-Optical translation framework that introduces a weight map throughout the training process. The proposed weight map combines Attribution maps and Uncertainty maps to amplify losses in important regions while guiding conservative learning in uncertain areas. Moreover, the weight map is incorporated into the registration stage to refine pixel-wise displacement estimation, preserve boundary and structural consistency, and enhance overall training stability. The experimental results demonstrate that the proposed method outperforms existing approaches on the SAR2Opt and SEN1-2 datasets in all metrics, including PSNR and SSIM.