Urban green spaces, crucial for ecological balance, face global degradation from natural disasters and rapid urbanization. Manual deforestation monitoring is laborious, prompting a shift to remote sensing and bitemporal satellite imagery. Traditional change detection (CD) methods have limitations, but deep learning, especially in semantic CD, shows promise. This study addresses challenges in semantic CD techniques, advocating for comprehensive training on datasets covering both semantic change masks and binary change masks. We propose a novel semantic CD network for urban changes while additionally providing urban greenery increased and decreased regions, integrating deep bitemporal features with an encoder-decoder structure, Atrous spatial pyramid pooling, and a spatial attention module with parallel dilated convolutions. Quantitative assessment, especially with pre-trained VGG16 as a backbone and parallel convolutional layers, demonstrates the proposed method's superiority, showcasing substantial improvements in urban greenery CD alongside overall urban changes. The proposed method holds potential for monitoring climate change, rapid urbanization, and the impact of natural disasters on urban environments, particularly urban greenery.

Korean multi-purpose satellite 3A (KOMPSAT-3A) can acquire electric optical (EO) and mid-wave infrared (MIR) images. Since MIR and EO images provide different information, they can be used together to effectively observe various phenomena on the Earth's surface. However, geometric misalignments exist between the EO and MIR images as the difference in the positions of each sensor when acquiring the images. In this study, we propose a hybrid pyramid matching (HPM) method to conduct the image registration between heterogeneous EO and MIR images with different spatial and spectral characteristics. The HPM method extracts reliable tie points (TPs) by iteratively adjusting the location of local templates in pyramid image pairs. Then, the image registration is conducted using transformation matrix estimated based on the TPs. The HPM method achieved superior accuracy and performance at three different sites.

Cloud cover is a significant obstacle to use optical satellite imagery. Therefore, various studies have been proposed to accurately detect clouds and evaluate satellite image quality. In particular, with the advancement of deep learning technology, many cloud detection studies are being conducted. However, a large volume of high-quality data is required to develop an effective deep learning model training. Thus, in this study, we compare the performance of deep learning cloud detection models for according to the diversity of sensors and resolutions of training data. For conducting the study, five case dataset combinations were constructed and trained with HRNet (High-Resolution Network). The performance evaluation of the trained models was conducted using test images from the KOMPSAT and PlanetScope satellites. As a mean of achieving high cloud detection results, it was found that selecting and using high-quality data is more effective than simply increasing the number of training data.

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.

Very-high-resolution (VHR) satellite image contains reliable various information over large areas, so that, it has been used as key data in the field of remote sensing. Image registration must be conducted to effectively use the multitemporal VHR satellite images. Conjugate points (CPs) extracted from the same region between images are required to perform image registration. However, outliers included in the CPs cause distortion when they were used for the image registration. Here we propose a deep learning-based technique to effectively remove the outliers. A Siamese network was built as a purpose of an outlier removal, and the network was trained using data based on the patch pirs centered on each CP. Experimental results demonstrate that the proposed method can remove outliers more effectively than a random sample consensus (RANSAC) technique thus and achieves improved registration accuracy.

Since multi-temporal very-high-resolution (VHR) satellite images generally have geometric misalignment, image co-registration process is required to minimize it. To perform precise image co-registration, extraction of reliable conjugate points (CPs) is an important process. Moreover, CPs extracted from elevated objects can cause severe relief displacements according to acquisition angles of images. In this study, the effect of CPs extracted from buildings on co-registration performance was analyzed. To this end, CPs were extracted using a method that combines feature-based and area-based matching methods, and digital map was used to remove CPs extracted from the buildings. Root mean square errors (RMSE) was calculated using manually obtained checkpoints to evaluate the accuracy of co-registration according to the presence or absence of CPs extracted on buildings. When CPs extracted from buildings were removed, the RMSE of the checkpoints extracted from the dense-building area was improved by more than 4 pixels.