Existing satellite remote sensing change detection (CD) methods often crop large-scale bi-temporal image pairs into small patch pairs and then use pixel-level CD methods for fair processing. However, due to the sparsity of change, existing pixel-level methods suffer from a waste of computational cost and memory resources on many unchanged areas, which reduces the processing efficiency on hardware platforms with extremely limited computation and memory resources. To address this issue, we propose a lightweight patch-level CD network (LPCDNet) to rapidly remove lots of unchanged patch pairs in large-scale bi-temporal optical image pairs, helping to accelerate the subsequent pixel-level CD process and reduce memory cost. In our LPCDNet, a sensitivity-guided network pruning method is proposed to remove unimportant channels and construct the lightweight backbone network on basis of the ResNet18 network. Then, the multi-layer feature compression (MLFC) module with multi-scale max-pooling structure is designed to compress and fuse the multi-level feature information of image patches. The output of MLFC module is fed into the fully-connected decision network to generate the predicted binary label. Finally, a weighted cross-entropy loss is utilized in the training process to tackle the change/unchanged class imbalance problem. Experiments on two CD datasets demonstrate that our LPCDNet achieves more than 1000 frames per second on an edge computation platform, i.e., NVIDIA Jetson AGX Orin, which is more than 3 times that of the existing methods without noticeable performance loss. In addition, the computational cost of the pixel-level CD processing stage can be reduced by more than 60%.

Unsynchronization of the separate transmit–receive beams makes it a challenging task to obtain high-quality images for nonparallel-track bistatic synthetic aperture radar (NP-BiSAR). To accommodate this issue, we propose an imaging configuration where the receiver’s beam follows the transmitter’s one actively by adjusting the squint angle of receiver. And a frequency extended nonlinear chirp scaling (FENLCS) algorithm is modified to cope with new effects introduced by the innovative configuration, which is based on an improved quadratic ellipse model. Based on the new model, some innovative improvements on image formation are made, including a residual azimuth-dependent high-order range cell migration correction (ADH-RCMC) and a rederived FENLCS that takes highly varying Doppler centroid into consideration, which contribute to better imaging quality. Simulation results validate the effectiveness of the proposed configuration and algorithm.

Lake shoreline detection plays an important role in hydrological structure analysis and urban ecology governance but is a challenging task in synthetic aperture radar (SAR) image interpretation. Due to the complex shoreline environment, the preservation of weak boundaries and fitting of global information in large-scale SAR images deserve further research. Thus, this letter proposes a novel coarse-to-fine lake shoreline detection approach for SAR images based on modified region-scalable fitting (RSF) model combined with edge energy and global energy. In this approach, SAR images are despeckled by the block-matching 3-D (BM3D) filter. Then a novel energy term based on Laplacian of Gaussian (LoG) operator and ratio of exponentially weighted averages (ROEWA) operator is constructed to accurately locate the boundary and reduce false boundary. Additionally, the global energy term is adopted to fit the global information well. The experimental results based on real data demonstrate that the proposed approach has a stronger ability to maintain weak edges compared with RSF model, which exhibits better effectiveness and reliability.

Distributed scatterer (DS) interferometric synthetic aperture can retrieve maximum available information by jointly processing persistent scatterers (PSs) and DSs. Unlike PSs, DSs are vulnerable to temporal, geometrical, and volumetric decorrelation. The phase optimization of DSs is essential for reliable parameter estimation. However, the preprocessing of DSs is very computationally expensive, and this drawback limits its engineering application to some degree. To improve computational efficiency, a fast scheme for reliable phase optimization of DSs is proposed based on the coherence-weighted model in this letter. The Gauss–Seidel iteration, having the advantages of fast convergence rate and small data memory, is introduced to solve the adopted phase optimization model. Experiments both on simulated data and real data are used to verify the reliability and efficiency of the presented method in this letter.

The linear range walk correction (LRWC) and the inherent azimuth-variant geometric configuration in the case of one-stationary bistatic synthetic aperture radar (OS-BiSAR) imaging produce 2-D variant range cell migrations (RCMs) and azimuth-dependent Doppler parameters, which makes it more difficult to obtain high-quality image for high-resolution and highly squinted OS-BiSAR. To accommodate these issues, an improved extended nonlinear chirp scaling (ENLCS) algorithm is developed in this letter. An analytic model based on the equi-sum of bistatic ranges (ESBR) after the RCM correction is proposed to reveal the azimuth-variant property of the azimuth distributed targets. Based on this innovative model, analytic coefficients of the ENLCS are derived, and high-quality image formation for OS-BiSAR is accomplished. Simulations are conducted to demonstrate the validity of the proposed algorithm.

The multitemporal interferometric synthetic aperture radar (InSAR) technique is a potential tool for measuring digital elevation models and surface deformation. It has the advantage of high precision, competitive spatial resolution, and wide coverage. To improve the accuracy of the final results, some adaptive multilook strategies have been proposed in which the identification of statistically homogeneous pixels (SHPs) is the key task. However, these methods are not always reliable in the case of small data sets. To improve this reliability, SHPs are identified based on the adaptive joint data vector comprising of temporal sample and spatial information in this letter. Additionally, the formulation of adaptive joint data vector is combined with local spatial features of SAR images. The presented adaptive multilook approach can be used in many interferometric applications, such as InSAR data filtering and coherence estimation. Experiments on six TerraSAR-X stripmap images of Tianjin in China validate the feasibility and effectiveness of the proposed approach.

It has been proven that SqueeSAR technique has validated the potential to increase the density of measure points (MP) involving persistent scatter (PS) and distributed scatter (DS) candidate in nonurban areas. In SqueeSAR, DS candidate exhibiting high extended temporal coherence can be processed jointly with PS for deformation estimation after phase estimation by the phase triangulation algorithm (PTA). The PTA extracts phase values of DS by using all possible interferograms under the Gaussian scattering assumption. However, the statistics hypothesis, Gaussian random variable, is no more applicable, with radar resolution increasing. More precisely, it has been shown that clutter in high resolution SAR images can be modeled as a compound Gaussian process. In this letter, a modified approach to phase estimation of DS named MPTA is proposed based on the compound Gaussian model. Experiments on real data are presented to demonstrate the effectiveness of the proposed method.

Statistically Homogeneous Pixels (SHPs) selection is a significant step of multi-temporal interferometric synthetic aperture radar (InSAR) for Distributed Scatterers (DS). A series of studies namely, Anderson-Darling test (AD test) and its variants, have demonstrated their advantages. However, these algorithms have a similar drawback that they put little attention on the spatial amplitude distributions and cost too much time of processing. To solve the problem, this paper proposes a modified statistically homogeneous pixels selection algorithm (MoSHPS). It utilizes the amplitude values to get the prior information of the images through an unsupervised classifier, in order to guide the SHPs selection. It can improve the accuracy for SHPs selection, and promote the computing efficiency. In the end, results on a series of real TerraSAR-X datas, acquired over a Tianjin area, confirm the effectiveness of this algorithm.

Statistically homogeneous pixels (SHPs) are considerably significant in many interferometric applications, such as interferometric filtering, distributed scatterer selection, small baseline subset, and SqueeSAR processing. It is very important to achieve SHPs efficiently and accurately. Previous studies on SHPs' selection are based on spatial restrictions and likelihood ratio test, such as Lee filtering, Kolmogorov-Smirnov test, and Anderson-Darling test. However, these algorithms do not stand up to test for the spatial similarity hypothesis for complex terrains with a few images. To solve the problems, this letter proposes a modified SHPs' selection algorithm. It utilizes geometric distance and target features for reaching a priori information to help the similarity hypothesis tests. The proposed algorithm has been tested on simulated and real data to prove the improvements in terms of accuracy and computational efficiency.