Traditional constant false alarm rate (CFAR) detectors suffer probability of detection (PD) degradation influenced by the outliers such as interfering ship targets, side lobes, and ghosts, especially in crowded harbors and busy shipping lines. In this paper, a new two-parameter CFAR detector based on adaptively truncated clutter statistics (TS-LNCFAR) is proposed. The new two-parameter CFAR detector uses log-normal as the statistical model; by adaptive-threshold-based clutter truncation in the background window, the outliers are removed from the clutter samples, while the real clutter is preserved to the largest degree. The log-normal model is accurately built using the truncated clutter statistics through the maximum-likelihood estimator. Compared with traditional CFAR detectors, the parameter estimation is more accurate, and TS-LNCFAR has a better false alarm regulation property and a high PD in a multiple-target environment. Furthermore, the parameter estimation and threshold calculation do not need iterative numerical calculation, and TS-LNCFAR has a high computational efficiency. The superiority of the proposed TS-LNCFAR detector is validated on the multilook Envisat-ASAR and TerraSAR-X data.

At present, it is challenging to extract landslides from high-resolution remote-sensing images using deep learning. Because landslides are very complex, the accuracy of traditional extraction methods is low. To improve the efficiency and accuracy of landslide extraction, a new model is proposed based on the U-Net model to automatically extract landslides from remote-sensing images: L-Unet. The main innovations are as follows: (1) A multi-scale feature-fusion (MFF) module is added at the end of the U-Net encoding network to improve the model’s ability to extract multi-scale landslide information. (2) A residual attention network is added to the U-Net model to deepen the network and improve the model’s ability to represent landslide features. (3) The bilinear interpolation algorithm in the decoding network of the U-Net model is replaced by data-dependent upsampling (DUpsampling) to improve the quality of the feature maps. Experimental results showed that the precision, recall, MIoU and F1 values of the L-Unet model are 4.15%, 2.65%, 4.82% and 3.37% higher than that of the baseline U-Net model, respectively. It was proven that the new model can extract landslides accurately and effectively.

As one of China’s new generation polar-orbiting meteorological satellites, FengYun-3D (FY-3D) provides critical data for forest fire detection. Most of the existing related methods identify fire points by comparing the spatial features and setting thresholds empirically. However, they ignore temporal features that are associated with forest fires. Besides, they are difficult to generalize to multiple areas with different environmental characteristics. A novel method based on FY-3D combining the genetic algorithm and brightness temperature change detection is proposed in this work to improve these problems. After analyzing the spatial features of the FY-3D data, it adaptively detects potential fire points based on these features using the genetic algorithm, then filters the points with contextual information. To address the false alarms resulting from the confusing spectral characteristics between fire pixels and conventional hotspots, temporal information is introduced and the “MIR change rate” based on the multitemporal brightness temperature change is further proposed. In order to evaluate the performance of the proposed algorithm, several fire events occurring in different areas are used for testing. The Moderate-Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire products (MYD14) is chosen as the validation data to assess the accuracy of the proposed algorithm. A comparison of results demonstrates that the algorithm can identify fire points effectively and obtain a higher accuracy than the previous FY-3D algorithm.

Traditional constant false alarm rate (CFAR) detectors only use the contrast information between ship targets and clutter, and they suffer probability of detection (PD) degradation in multiple target situations. This paper proposes a correlation-based joint CFAR detector using adaptively-truncated statistics (hereafter called TS-2DLNCFAR) in SAR images. The proposed joint CFAR detector exploits the gray intensity correlation characteristics by building a two-dimensional (2D) joint log-normal model as the joint distribution (JPDF) of the clutter, so joint CFAR detection is realized. Inspired by the CFAR detection methodology, we design an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers, such as interfering ship targets, side-lobes, and ghosts in the background window, whereas the real clutter samples are preserved to the largest degree. A 2D joint log-normal model is accurately built using the adaptively-truncated clutter through simple parameter estimation, so the joint CFAR detection performance is greatly improved. Compared with traditional CFAR detectors, the proposed TS-2DLNCFAR detector achieves a high PD and a low false alarm rate (FAR) in multiple target situations. The superiority of the proposed TS-2DLNCFAR detector is validated on the multi-look Envisat-ASAR and TerraSAR-X data.

A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure optimization and small sample augmentation. For model structure optimization, the first approach is to extract deep features of the target with residual networks instead of with VGGNet. Then, the aspect ratios of the default boxes are redesigned to match the different targets’ sizes. For small sample augmentation, besides the routine image processing methods, such as rotating, translating, and mirroring, enough training samples are obtained based on the saliency map theory in machine vision. Lastly, a simulated SAR image dataset called Geometric Objects (GO) is constructed, which contains dihedral angles, surface plates and cylinders. The experimental results on the GO-simulated image dataset and the MSTAR real image dataset demonstrate that the proposed method has better performance in SAR target detection than other detection methods.

In multiple-input multiple-output synthetic aperture radar (MIMO–SAR) signal processing, a reliable separation of multiple transmitted waveforms is one of the most important and challenging issues, for the unseparated signal will degrade the performance of most MIMO–SAR applications. As a solution to this problem, a novel APC–MIMO–SAR system is proposed based on the azimuth phase coding (APC) technique to transmit multiple waveforms simultaneously. Although the echo aliasing occurs in the time domain and Doppler domain, the echoes can be separated well without performance degradation by implementing the azimuth digital beamforming (DBF) technique, comparing to the performance of the orthogonal waveforms. The proposed MIMO–SAR solution based on the APC waveforms indicates the feasibility and the spatial diversity of the MIMO–SAR system. It forms a longer baseline in elevation, which gives the potential to expand the application of MIMO–SAR in elevation, such as improving the performance of multibaseline InSAR and three-dimensional SAR imaging. Simulated results on both a point target and distributed targets validate the effectiveness of the echo separation and reconstruction method with the azimuth DBF. The feasibility and advantage of the proposed MIMO–SAR solution based on the APC waveforms are demonstrated by comparing with the imaging result of the up- and down-chirp waveforms.