Multimodal remote sensing image matching is a challenging task because of the existence of significant radiometric differences. To address the problem, we develop a novel multimodal remote sensing image matching method based on self-similarity features. The offset mean filtering method is proposed first to calculate the self-similarity features fast based on the symmetry of the self-similarity. The self-similarity features are presented through a multichannel self-similarity map (SSM) and a corresponding multichannel symmetric SSM. On this basis, we develop the image matching method, including a feature detector named improved maximal self-dissimilarities (IMSD) and a feature descriptor named oriented self-similarity (OSS). The IMSD detector is designed by introducing the two multichannel SSMs into the maximal self-dissimilarities (MSD) detector for feature point detection. The OSS descriptor is proposed based on the orientations of the self-similarities extracted from the multichannel SSMs. We conduct experiments with a variety of optical, synthetic aperture radar, and light detection, and ranging data. Our results demonstrate the advantages of our proposed IMSD detector and OSS descriptor in comparison with state-of-the-art detectors and descriptors, respectively. The image registration results further confirm the effectiveness of the proposed method.

Because optical and synthetic aperture radar (SAR) images are complementary, their registration has received extensive attention in joint applications. However, robust optical and SAR image registration is challenging due to substantial geometric and radiometric differences. To address this problem, we propose a fast and robust registration algorithm for optical and SAR images based on a novel feature type known as the adjacent self-similarity (ASS). The ASS feature of the pixelwise feature representation is defined to quickly and finely capture the structural features of the image. The ASS feature is extracted by using an optimized offset mean filtering method in a neighborhood of the unit pixel radius to accelerate and refine calculations and the local statistics weighted difference operation to suppress coherent speckles. Based on the ASS feature, we extract the minimum self-similarity map (SSM) and the index map, which are robust against radiometric differences and speckles. Then, based on the excellent characteristics of the two maps, we propose a feature detector based on suppressing the local nonmaximum on the minimum SSM and a novel feature descriptor based on calculating the distribution histogram of the index map in a log-polar grid. In addition, we design a rotation invariance enhancement method for the descriptor to improve the rotation invariance robustness of the algorithm. We conduct experiments with both synthetic and real image pairs. The registration results demonstrate that the proposed algorithm has good scale and rotation invariance, as well as good antinoise ability, and that the algorithm performs better than existing state-of-the-art algorithms in terms of registration robustness, accuracy, and efficiency. The registration results on two real optical and SAR image pairs with complex image scenes show the adaptability of the proposed algorithm.

Modern armed conflicts can cause serious humanitarian disasters, and remote sensing technology is critical in monitoring war crimes and assessing post-war damage. In this study, a constrained energy minimization algorithm incorporating the feature bands (IFB-CEM) is designed to detect urban burning areas in optical images. Due to the difficulty of obtaining the ground survey data of the battlefield, the dual-polarization normalized coherence index (DPNCI) is designed based on the multi-temporal synthetic aperture radar (SAR) image, and the quantitative inversion and evaluation of the destruction of urban architecture are combined with the public images on the Internet. The results show that the burning area is widely distributed in the armed conflict region, and the distribution is most concentrated around the Azovstal steel and iron works. The burning area reached its peak around 22 March, and its change is consistent with the conflict process in time and space. About 79.2% of the buildings in the city were severely damaged or completely destroyed, and there was a significant correlation with burning exposure. The results of this study show that publicly available medium-resolution remote sensing data and Internet information have the ability to respond quickly to the damage assessment of armed conflict and can provide preliminary reference information for dealing with humanitarian disasters.

Synthetic aperture radar (SAR) images in different polarizations or from different sensors are becoming easily available, but registering these images is challenging because of the presence of significant speckles in SAR images and the existence of radiometric differences between images. To address the problems, we propose a novel feature descriptor named rank-based ratio self-similarity (RRSS) for robust SAR image registration. The descriptor first calculates a ratio surface by replacing the distance surface, as the use of the ratio is more robust to multiplicative noise, and then sorts the ratio values to construct the rank surface. Subsequently, the rank surface is partitioned into an index map, and the index map is then transformed into the descriptor vector based on a restricted adaptive binning grid to discriminatively describe features. Furthermore, a rotation invariance enhancement method is designed for the RRSS descriptor to efficiently calculate descriptor vectors in multiple orientations. We conduct experiments with six SAR image pairs of various bands, polarizations, and resolutions from different sensors, including ALOS-PALSAR, Gaofen-3, Sentinel-1, and TerraSAR-X. The results demonstrate that the proposed descriptor is superior to state-of-the-art descriptors and robust for SAR image registration.

We present a novel echo-based synthetic aperture radar (SAR) image simulation method that comprehensively utilizes both SAR effective view and ray tracing algorithms. To improve the fidelity of simulated SAR images, we first design an SAR effective view algorithm to process the selected facet target model, with the purpose of discretizing the facets in the SAR effective view into lattice targets and ensuring that the interval of lattice targets is set strictly following the Nyquist sampling law. Then, we combine the ray tracing algorithm and SAR echo time-domain simulation and perform SAR echo time-domain simulation of lattice targets based on ray tracing. Various kinds of backscatter coefficient for each point target can be recorded, corresponding to the number of transmitted pulses within the synthetic aperture time. The echo matrixes of lattice targets are superimposed to generate the raw echo signal. Finally, the raw echo signal is processed by using the range-Doppler (RD) imaging algorithm to obtain the simulated SAR image. We conducted SAR image simulation tests on several facet target models, including car body, assault boat, and airplane, with different material properties. The simulated SAR images obtained by the proposed method were qualitatively and quantitatively evaluated. The experimental results verify the effectiveness of the proposed method.