Compact polarimetric synthetic aperture radar (CP SAR), as a new technique or observation system, has attracted much attention in recent years. Compared with quad-polarization SAR (QP SAR), CP SAR provides an observation with a wider swath, while, compared with linear dual-polarization SAR, retains more polarization information in observations. These characteristics make CP SAR a useful tool in marine environmental applications. Previous studies showed the potential of CP SAR images for ship detection. However, false alarms, caused by ocean clutter and the lack of detailed information about ships, largely hinder traditional methods from feature selection for ship discrimination. In this paper, a segmentation method designed specifically for ship detection from CP SAR images is proposed. The pixel-wise detection is based on a fully convolutional network (i.e., U-Net). In particular, three classes (ship, land, and sea) were considered in the classification scheme. To extract features, a series of down-samplings with several convolutions were employed. Then, to generate classifications, deep semantic and shallow high-resolution features were used in up-sampling. Experiments on several CP SAR images simulated from Gaofen-3 QP SAR images demonstrate the effectiveness of the proposed method. Compared with Faster RCNN (region-based convolutional neural network), which is considered a popular and effective deep learning network for object detection, the newly proposed method, with precision and recall greater than 90% and a F₁ score of 0.912, performs better at ship detection. Additionally, findings verify the advantages of the CP configuration compared with single polarization and linear dual-polarization.

Previous damage detection of frame structures mainly focuses on the detection of beam and column element damage. It has been shown that beam-column joints in frame structures are more susceptible to damage than the other members in the structure. Joint damage may be represented by the reduction of beam-column connection rigidity. Therefore, damage detection of a frame structure with joint damage includes the identification of joint connection stiffness in additional to those of beam and column stiffness, which involves the difficulty of identifying a large number of unknown structural parameters. In this paper, an algorithm based on a two-step Kalman filter approach is presented for the damage detection of frame structures with joint damage under earthquake excitation using partial measurements of structural acceleration responses. Recursive solutions for unknown structural parameters and structural state vector are derived by a two-step Kalman filter, respectively. Therefore, the number of unknown variables to be estimated in each step is reduced compared with the conventional Extended Kalman filter (EKF) approach. Structural damage is detected from the degradation of the identified stiffness values of joints, beam and column elements of frame structures. A numerical example and a lab experiment test data are used to validate the performances of the proposed algorithm for damage identification of various joint damage scenarios in frame structures under earthquake excitation.

Landsat 4-5, built at the same time and with the same design, carrying the Multispectral Scanner System (MSS) and the Thematic Mapper (TM) simultaneously, jointly provided observation service for about 30 years (1982–2013). Considering the importance of data continuity for time series analyses, investigations on the continuity of the Landsat 4-5 MSS and TM are required. In this paper, characterization differences between the Landsat 4-5 MSS and TM were initially discussed using the synthesized reflectance records generated from a collection of Hyperion hyperspectral profiles which were well calibrated and widely distributed. The difference in near-infrared region mostly contributed to the difference in normalized difference vegetation index (NDVI) between MSS and TM, while the between-sensor difference in red spectrum was relatively minor. Models for transforming MSS NDVI to TM NDVI were proposed, and validated subsequently through cross-validation tests. Furthermore, effectiveness of the transformation models was investigated using eight synchronous observation pairs of the Landsat 5 MSS and TM. On average, the univariate models through ordinary least squares regression (OLS) regression resulted in a decrease about 10% of the median relative difference (MdRD). Meanwhile, the bivariate models improved the NDVI comparability in most cases, especially when the transformation models through ridge regression were implemented. The univariate model through OLS regression could be the only solution for cases when problems of data quality are encountered (e.g., problem in the MSS near-infrared channel (800–1000 nm)). In conclusion, the findings on NDVI transformation models from MSS to TM are valuable for reference, because of the collection of diverse Hyperion hyperspectral profiles used.

The Landsat 1-5 multispectral scanner system (MSS) collected records of land surface mainly during 1972-1992. Investigations on MSS have been relatively limited compared with the numerous investigations on its successors, such as Thematic Mapper (TM) and Enhanced TM Plus (ETM+). The benefits of the Landsat program are not fully accomplished without the inclusion of MSS archives. Investigations on the Landsat 1-5 MSS channel reflectance characteristics wereperformed followed by derived vegetation spectral indices and the Tasseled Cap (TC) transformed features mainly using a collection of synthesized records. On average, the Landsat 4 MSS is generally comparable to the Landsat 5 MSS. The Landsat 1-3 MSSs show disagreement in channel reflectance compared with the Landsat 5 MSS, especially for the red channel (600-700 nm) and the near-infrared channel (700-800 nm). Meanwhile, the relative differences for vegetation spectral indices of the Landsat 3 MSS are mainly from -16% to -5% with the median about -11.5%, while those of the Landsat 2 MSS are mainly from -15% to -7%. Cross-validation tests and two case applications suggested that between-sensor consistency was improved generally through the transformation models generated by ordinary least-squares regression. To improve the consistency of the vegetation indices and the TC greenness, direct strategy employing respective transformation models was more effective than calculations based on the transformed channel reflectance. Considering the shortages of the Landsat MSS archives, further efforts are needed to improve its comparability with observations by other successive Landsat sensors.