Change detection using multi-sensor remote sensing images, such as synthetic aperture radar (SAR) and optical images, is poorly researched and thus remains a challenging task. In this study, we address this problem by proposing a novel automatic change detection method. Different sensors have completely different physical principles. Thus, the resulting multi-sensor images have completely different radiometric values. First, we introduce a sorted histogram concept that sorts the bins in descending order, noticing that multi-sensor images with absence of change have the same combination of objects, and each object in different images has the same proportions and a unique range of grey values. The sorted histogram discards the visual property correspondence between images and is capable of capturing the local internal image layout. Then, various histogram-based distances are employed to estimate the distance between each sorted histogram pair. After the whole image has been analysed, we obtain a divergence index map. The sorted histogram not only has the theoretical advantage of robustness in the intensity variations in multi-sensor images but also the practical advantage of low computational complexity compared with existing methods. Experiments on SAR and optical datasets with different resolutions show promising results in terms of detection capability and run time.

The fuzzy c-means (FCM) algorithm and many improved algorithms incorporating spatial information have been proven to be effective in image segmentation. However, these methods are not adaptable to process synthetic aperture radar (SAR) images owing to the intrinsic speckle noise. Our solution, which enables the effective segmentation of SAR images by guaranteeing noise-immunity and edge detail preservation simultaneously, is to propose a robust FCM algorithm based on Bayesian nonlocal spatial information (RFCM$\_$BNL). The nonlocal idea considers more useful information for generating an auxiliary image. We measure the similarity between patches by utilizing a dedicated noise model for SAR images, and then apply it to the Bayesian formulation. Then we derive a new statistical distance, which is insensitive to speckle noise. Additionally, we ensure that the algorithm is robust to outliers by employing the entropy of the local gray-level histogram to control the extent to which the nonlocal spatial information term is adaptive to pixels. Experiments on simulated and real SAR images show that RFCM$\_$BNL obtains the best result for SAR image segmentation compared with seven other fuzzy clustering algorithms.

Flood is one of the most frequent and widespread natural hazards globally, which can cause tremendous economic damage and human casualties. As such, flood event monitoring is essential, for which Synthetic Aperture Radar (SAR), with high spatial resolution as well as all-weather and all-time capabilities, can provide high-quality data support. However, algorithms for automatic flood inundation mapping that do not require ancillary data are limited. In this study, we propose a hybrid methodology that combines automatic thresholds selection, pixel- and object-based classification, and bidirectional region growing method for extracting flood inundation areas. This is a fully automatic approach that does not require the assistance of ancillary data. Firstly, the gamma distribution is used to estimate the probability density function (PDF) of ‘open water’ and to set thresholds. Then, we introduce a two-step classification approach, applying the pixel- and object-based classifications; the former is easy to implement with low computational complexity and stable performance, whereas the latter can reduce noise pixels and is less sensitive to SAR intrinsic speckle. The two-step classification is employed to yield core flooded and non-flooded regions that are used as seeds for region growing. Furthermore, we propose a bidirectional region growing approach that grows regions for flooded and non-flooded regions simultaneously to eliminate areas of uncertainty, while minimizing under- and over-detection. We verified the proposed approach by applying it to real flood events that occurred in Jilin, China on 13 July 2017 and 20 July 2017. The experimental results demonstrate the effectiveness and reliability of the proposed approach.

Navigation satellite signals have the advantages of all-day, all-weather, and global coverage, and the use of navigation signals for the detection of moving targets has significant application prospects. However, the GNSS signal is very weak, and the signal power is greatly attenuated after being scattered by the target. In order to detect the echo signal, a long integration time is required. However, the movement of the target will cause the echo signal to produce unpredictable range migration and a Doppler frequency shift, which will weaken the cumulative effect of long-term integration. This paper proposes a weak signal processing method with amplitude and phase self-correction for moving target detection in the GNSS-S radar. First, the phase consistency of the echoes of a single GNSS satellite is realized by the block expansion compression and phase differential correction method to improve the coherent accumulation gain; then, multi-star joint accumulation is carried out after the signal amplitudes of multiple satellites are corrected by the improved keystone method, so as to obtain a stable echo signal track. This method can effectively improve the integral gain of the scattering signal of the moving target and realize target detection. The simulation results and field tests show that this method can effectively improve the SNR of the GNSS-S signal and can realize the detection of small moving targets such as cars with GNSS-S radar.

Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth resolution and 240 MHz range bandwidth. In staring spotlight (ST) mode, the antenna always illuminates the same scene on the ground, which can extend the synthetic aperture. Based on a two-step processing algorithm, some special aspects such as curved-orbit model error correction, stop-and-go correction, and antenna pattern demodulation must be considered in image focusing. We provide detailed descriptions of all these aspects and put forward corresponding solutions. Using these suggested methods directly in an imaging module without any modification for other data processing software can make the most of the existing ground data processor. Finally, actual data acquired in GF-3 ST mode is used to validate these methodologies, and a well-focused, high-resolution image is obtained as a result of this focusing experiment.

This paper proposed an object-based multiscale method for synthetic aperture radar (SAR) image change detection based on the statistical model. Rather than the pixel-based analysis conducted in the traditional way, the object-based image analysis was employed to take a collection of pixels as the unit of analysis, which reduced small spurious changes and was less strict relative to registration. In addition, a multiscale concept was adopted to exhibit the inherent multiscale characteristics of the target. To achieve object-based, multiscale change detection results, the multidate segmentation was performed on two temporal SAR images and extended to a set of suitable scales. Then, the Edgeworth series expansion was employed to estimate the probability density function, and the Kullback–Leibler divergence was adopted to calculate the distance between pairs of pixel collections. Next, the divergence index maps were divided into changed and unchanged classes to obtain change detection results for each scale. Finally, the subresults were combined to obtain a more accurate detection result. The experimental results obtained using real data demonstrated the effectiveness of the proposed method.