This paper presents a robust change detection algorithm for high-resolution panchromatic imagery using a proposed dual-dense convolutional network (DCN). In this work, a joint structure of two deep convolutional networks with dense connectivity in convolution layers is designed in order to accomplish change detection for satellite images acquired at different times. The proposed network model detects pixel-wise temporal change based on local characteristics by incorporating information from neighboring pixels. Dense connection in convolution layers is designed to reuse preceding feature maps by connecting them to all subsequent layers. Dual networks are incorporated by measuring the dissimilarity of two temporal images. In the proposed algorithm for change detection, a contrastive loss function is used in a learning stage by running over multiple pairs of samples. According to our evaluation, we found that the proposed framework achieves better detection performance than conventional algorithms, in area under the curve (AUC) of 0.97, percentage correct classification (PCC) of 99%, and Kappa of 69, on average.

Remote sensing, particularly using synthetic aperture radar (SAR) systems, can be an effective tool in detecting and assessing the area and amount of building damages caused by earthquake or tsunami. Several studies have provided experimental evidence for the importance of polarimetric SAR observations in building damage detection and assessment, particularly caused by a tsunami. This study aims to evaluate the practical applicability of the polarimetric SAR observations to building damage caused by the direct ground-shaking of an earthquake. The urban areas heavily damaged by the 2016 Kumamoto earthquake in Japan have been investigated by using the polarimetric PALSAR-2 data acquired in pre- and post-earthquake conditions. Several polarimetric change detection approaches, such as the changes of polarimetric scattering powers, the matrix dissimilarity measures, and changes of the radar scattering mechanisms, were examined. Optimal damage indicators in the presence of significant natural changes, and a novel change detection method by the fuzzy-based fusion of polarimetric damage indicators are proposed. The accuracy analysis results show that the proposed automatic classification method can successfully detect the selected damaged areas with a detection rate of 90.9% and false-alarm rate of 1.3%.

Radar backscattering properties can be extremely sensitive to the freeze/thaw states of the ground surface. This study aims to evaluate the changes of L-band microwave scattering characteristics between thawed and frozen conditions by using polarimetric scattering mechanism indicators. ALOS PALSAR polarimetric mode data acquired in winter and spring seasons over Eastern Siberia are used in this study. Experimental results show that the actual scattering mechanisms and their seasonal variations over various forested and non-forested permafrost ecosystems can be successfully characterized by the polarimetric target decomposition parameters and the polarimetric coherences. In addition, fully polarimetric radar observations exhibit great potential for mapping land cover types and surficial features in the permafrost active layer. Particularly, the co-polarization coherences on the HV-polarization basis and circular-polarization basis were found to be very useful for discriminating different surficial geocryological characteristics in recently burnt forests and thermokarst regions.

The advantage of implementing the Water Cloud Model (WCM) is in being able to express complex scattering characteristics in a vegetated area with simple bulk vegetation descriptors. However, there has been a lack of understanding or consensus about the optimal set of vegetation descriptors. In this paper, the original and improved expressions of WCM are evaluated and the optimal vegetation descriptors are presented by examining the relationship between WCM vegetation parameters and the theoretical scattering model predictions. In addition, the condition-specific regression relationship between bulk vegetation descriptors and theoretical scattering and attenuation coefficients, expressed by the $A$ and $B$ parameters in the WCM, is analyzed in relation to the shape, size, and orientation distribution of the scatterer. Furthermore, the influence of radar observation conditions on the parameterization of the WCM is presented. The results show that the particle moisture content and the vegetation water content can be the optimal vegetation descriptors, denoted by the $V_1$ and $V_2$ variables in the WCM, respectively.

Synthetic Aperture Radar (SAR) remote sensing has been widely used as one of the most effective tools for responding to earthquake disasters. In general, damaged-building detection with SAR data has been conducted based on change detection using temporal SAR data acquired in the same observation mode. However, it is not always possible to use SAR data obtained with the appropriate observation mode in unexpected events such as natural disasters. This study aims to detect earthquake-induced damaged buildings using temporal SAR data having different observation modes. We presented a contextual change analysis method to map damaged buildings based on novel textural features. This study was conducted using the bi-temporal Komapsat-5 data obtained in different polarization modes. Experimental results for the area severely damaged by the 2016 Kumamoto earthquake showed that the proposed textural analysis can improve detectability in building-damaged areas while maintaining low false alarm rates in agricultural areas. According to the grid-based accuracy analysis, the proposed method can successfully detect the damaged areas with a detection rate of about 72.5% and false alarms of about 6.8% even on challenging data sets.

Remote sensing technologies, particularly with Synthetic Aperture Radar (SAR) system, can provide timely and critical information to assess landslide distributions over large areas. Most space-borne SAR systems have been operating in different polarimetric modes to meet various operational requirements. This study aims to discuss how much detectability can be expected in the landslide map produced from the single-, dual-, and quad-polarization modes of observation. The experimental analysis of the characteristic changes of PALSAR-2 signals showed that quad-polarization parameters indicating signal depolarization properties revealed noticeable landslide-induced temporal changes for all local incidence angle ranges. To produce a landslide map, a simple change detection method based on characteristic scattering properties of landslide areas was proposed. The accuracy assessment results showed that the depolarization parameters, such as the co-pol coherence and polarizing contribution, can identify areas affected by landslides with a detection rate of 60%, and a false-alarm rate of 5%. On the other hand, the single- or dual-pol parameters can only be expected to provide half the accuracy with significant false-alarms in areas with temporal variations independent of landslides.