Crop field edge is the key characteristic of agricultural crop management. Edge detection with dual-polarization SAR time series have been widely studied, with the data advantages of sensitivity to crop growth. Existing methods rarely consider time-varying dynamic polarimetric characteristics, making it difficult to detect complete crop field edges. Based on this, this proposes a joint edge strength, which combines two kinds of polarimetric distances with a novel spatial-temporal homogeneity measure. This measure applies spatial- and temporal-varying contexts to pre-identify edge and homogenous area, and adaptatively allocate various distances in one pixel. There are 14 Sentinel-1 SAR time series images are utilized to evaluate the proposed method. By the comparison of the visual differences, our method has lower missing rate and lower false alarm rate than conventional methods.

With the development of the polarimetric statistical measures, change detection algorithms have been widely applied in the flood disaster detection. The representative methods utilize the Wishart distance to generate the difference map between the pre- and the post-disaster data. Furthermore, the difference map is applied into the threshold segmentation to generate the binary result. However, the existing methods rarely consider the impact of rainfall on the polarimetric distance, which causes abnormal polarimetric differences and incorrect detection results. Based on the fact of different sensitivities of the co- and the cross-polarization intensity for the rainfall, this paper uses the cross ratio of two polarization intensities to eliminate the impacts of the rainfall events on the polarimetric distances. Then the Markov Random Field (MRF) model is utilized to detect the flood disaster with the initialization of OTSU threshold segmentation. Two Sentinel-1 SAR data in the Poyang Lake Basin is used to assess the effectiveness of our method. The results have demonstrated the superiority of our method over the conventional methods, especially in the rainy areas.

Precision agriculture management relies on the delineation of crop field edges. Multi-polarization SAR technology has the ability to penetrate clouds and capture morphological structures or moistures, suited for extracting crop field edges. Due to the time-dependent characteristics and phenological evolutions of crops, the methods with single-date data are difficult to detect complete edges. Moreover, the existing methods fail to extract the dynamic time-varying patterns, limiting the improvement of edge detection accuracy. Based on this, this paper proposes a novel crop field edge detection method based on the time-varying polarimetric characteristics. First, a spatial-temporal homogeneity measure is proposed to pre-identify the edge and homogenous area, for guiding the adaptive calculation of edge strength. Based on the time-series polarimetric stationarity and the trace moment estimation theory, the proposed measure enlarges the separating degree of various crop parcels. Second, a joint edge strength is proposed to enlarge strength contrast between edge and homogenous area. With the spatial-temporal homogeneity measure, it combines the similarity with the root mean square and the similarity with time-series average covariance matrix. Based on the advantages of two kinds of similarities, it highlights the field edges and reduces the impact of speckle noises. Evaluated by 8 quad-polarization and 14 dual-polarization SAR images, the proposed edge detection method achieves better visual presentations and detection accuracies than traditional methods. With the statistics of the signal-noise ratio (SNR), the joint edge strength also has higher strength contrast than conventional strengths. The relevant codes can be found in https://github.com/DawnHanGeo/TSPolEdge.git.

The uncertainty of crop phenological cycle is an important issue in crop classification with time series PolSAR data. The time series alignment algorithm represented by dynamic time warping (DTW) can supply a potential solution, which realigns curves based on shape matching, dealing with the distortion of feature curves caused by uncertain crop phenological development. However, previous studies mainly focused on shape characteristics of time-varying feature curves, which is hard to comprehensively evaluate the similarity degree of crop phenological cycles. Furthermore, it ignored the differences in scattering signal and polarimetric statistical distribution of crops, which limited the accuracy of crop classification. In this letter, a novel crop classification method based on phenology alignment is proposed. Firstly, the dual-branch time series alignment method is proposed, including the time-weighted dynamic time warping (TWDTW) alignment and the Wishart distance-based TWDTW (WD-TWDTW) alignment, which combines the feature curve characteristics and the polarimetric statistical information to correctly describe the similarity degree of phenological cycles. Secondly, a multi-similarity measure (including shape similarity, feature similarity and polarimetric similarity) is defined to improve capacity of crop discrimination. The multi-similarity measure can describe the differences of crop types from three aspects, including crop growth trend, growth status, and statistical distribution. The proposed method is evaluated with time series full-polarization Radarsat-2 data in Flevoland area. The results show that our method is superior to traditional method with single TWDTW alignment and shape similarity, and the corresponding overall accuracy is improved by 6%.

Deep learning methods have been proved outperforming the traditional methods in the field of hyperspectral image classification (HSIC). However, in pursuit of higher accuracy, HSIC networks have become deeper and more complex, resulting in excessive parameters and computational cost. To deploy neural networks on small platforms such as mobile or embedded devices, many studies have focused on lightweight HSIC networks. Currently, these researches are dominated by patch-based networks, which suffer from the low accuracy caused by lightweight scale and slow inference speed derived from structural deficiencies of such networks. It is worth noting that full convolutional networks are able to achieve fast inference, but they tend to consume massive memory. To this end, this paper proposes a novel lightweight HSIC method, which consists of a successive spatial rectified network (SSRNet) and a non-central positional sampling (NCPS) strategy. SSRNet is composed of a local channel attention based spectral full convolutional network and several separable atrous spatial pyramid modules. These shallow sub-networks are concatenated together to progressively optimize their outputs by successive spatial rectified learning. For decreasing memory access cost, SSRNet makes little patches as input to perform patch-wise pixels-to-pixels learning. After training, SSRNet is able to adapt to any size of hyperspectral images and complete fast inference of the full image directly. In particular, the NCPS sampling strategy enables all labeled pixels to equally traverse all spatial positions of each training patch through the positional shift sampling, which effectively alleviates the sparse problem of hyperspectral semantic labels. Experiments upon three public benchmark datasets indicate that SSRNet is comparable to the state-of-the-art methods in classification accuracy with less than 0.15M parameters and only occupy less than 10MB memory for single forward computation. Moreover, SSRNet behaves significantly superior to the traditional patch-based networks in term of the inference speed. The source codes can be available from the website of https://github.com/Pancakerr/HSIC-platform.

A novel distributed scatterer interferometric synthetic aperture radar (DS-InSAR) method is presented in which the sum of Kronecker product (SKP) decomposition method is applied to DS candidates. Unlike existing polarimetric optimization methods, the proposed method considers polarimetric and interferometric coherence information simultaneously, resulting in separation of the polarimetric scattering process for each target and the maximum diversity for the corresponding phase center locations. Physical reliability of the phase optimization solution is thereby ensured. The performance of the novel method is evaluated using 30 quad-polarized C-band Radarsat-2 synthetic aperture radar (SAR) images over Kilauea Volcano, Hawaii. The proposed method provides a higher density of measurement scatterer (MS) points and a higher quality of DS interferometric phase with a temporally stable phase center than single-polarization (HH) method and quad-polarization exhaustive search polarimetric optimization (ESPO) method. Thus, the proposed method shows good performance in phase quality improvement and point density increasement.

Crop rotation is subsidized by the government because of its many advantages. Monitoring whether crop rotation is beneficial for agricultural management, and can also provide a reference for government subsidy policies for crop rotation. In this paper, we propose an unsupervised object-oriented crop rotation detection method using time-series polarimetric SAR (PolSAR) data. On the one hand, we construct the change detection matrix based on the likelihood ratio test (LRT) distance to perform temporal filtering. Then, the pixel-level temporal change image is generated using Shannon entropy and maximum between-class variance (OTSU). On the other hand, we perform temporal segmentation on time-series PolSAR images to obtain superpixel results. Finally, the object-level crop rotation results are obtained with the probabilistic label relaxation (PLR) model. 42 Sentinel-1 dual-polarization SAR datasets during 2018 and 2019 are selected for detecting crop rotation changes on farms within Jinchang, China. Experimental results show that the crop rotation detection accuracy andKappacoefficients of this method can reach 96.21% and 0.8989, respectively.

The superpixel generation is a key step for object-based classification and change detection. For the time-series polarimetric synthetic aperture radar (PolSAR) superpixel generation, the traditional polarimetric similarity measure based on the joint covariance matrix has limitations in discriminating different time-series similarity sequences with different fluctuations. Besides, in the traditional time-series PolSAR superpixel generation methods, it is difficult to determine the tradeoff factor between polarimetric and spatial similarity. In this article, an adaptive time-series PolSAR superpixel generation method based on the simple local iterative clustering (SLIC) is proposed, named time-series polarimetric SAR (TSPol)-adaptive simple local iterative clustering (ASLIC). There are three main improvements. First, a novel time-series polarimetric similarity measure based on the root mean square (rms) is proposed. Multitemporal polarimetric statistical information is combined to describe the polarimetric proximity between pixels, referring to the rms of the multitemporal proximities. Second, an edge detection method based on the stacked 2-D Gaussian-shaped (s2-D GS) window is proposed to initialize the central seeds for superpixel generation. Third, an improved SLIC clustering similarity combined with the time-series polarimetric, time-series power, and spatial similarities is proposed. Meanwhile, a homogeneity factor is applied to adaptively balance the relative weights of various similarities. We use eight Radarsat-2 quad-polarization synthetic aperture radar (SAR) images and 14 Sentinel-1 dual-polarization SAR images to evaluate the effectiveness. The results show our similarity measure and superpixel generation results are superior to those of the traditional methods. For example, as for the Radarsat-2 data, the improvement of the boundary recall by the proposed similarity measure and homogeneity factor is about 4% and 10%, respectively.

Multitemporal Sentinel-1 data sets are suitable for high-precision agricultural classification mapping due to its short revisit period and dual-polarization channels. At present, more and more attention has been paid to the multitemporal classification methods with feature curve matching, because the time-varying polarimetric characteristics show great potential to crop classification. However, current methods only use the variation of single intensity feature, and the indicators for evaluating similarity have not considered the effect of the variable growing seasons of different parcels. Based on this, a new method with feature curve matching is proposed, which uses the combination of multiple features and applies the discrete Fréchet distance and the Pearson distance to evaluate the similarity between two curves. The proposed method applies time-series Sentinel-1 images for crop classification in two study areas of Gansu province, China. The results show that the overall accuracies in two study areas of the proposed method are 94.98% and 90.20%, respectively. This method achieves higher classification accuracies, compared with the SVM classification method and some other methods with feature curve matching.

A novel method based on the optimal normal matrix constraint and cross-iteration algorithm is proposed in this letter to estimate the forest height using the polarimetric interferometry synthetic aperture radar (PolInSAR) data. First, to avoid the null ground-to-volume ratio assumption of the three-stage method, we use the PolInSAR optimal normal matrix constraint method to find out the pure volume coherence. This method can also provide a more accurate initial value for the least-squares iteration. Second, the cross-iteration is used for the forest height inversion, which provides better selection of the best polarization channel and solves the ill-conditioned matrix in the traditional least-squares iteration algorithm. This new method is validated using the BioSAR 2008 P-band data. The results show that the proposed method achieves an average accuracy of 2.6 m, which is better than that of the three-stage inversion method [root-mean-square error (RMSE) = 5.89 m] and the 6-D nonlinear iteration method (RMSE = 4.42 m).