Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

Crop intensity information describes the productivity and the sustainability of agricultural land. This information can be used to determine which agricultural lands should be prioritized for intensification or protection. Time-series data from remote sensing can be used to derive the crop intensity information; however, this application is limited when using medium to coarse resolution data. This study aims to use 3.7 m-PlanetScope™ Dove constellation data, which provides daily observations, to map crop intensity information for agricultural land in Magelang District, Indonesia. Two-stage histogram matching, before and after the monthly median composites, is used to normalize the PlanetScope data and to generate monthly data to map crop intensity information. Several methods including Time-Weighted Dynamic Time Warping (TWDTW) and the machine-learning algorithms: Random Forest (RF), Extremely Randomized Trees (ET), and Extreme Gradient Boosting (XGB) are employed in this study, and the results are validated using field survey data. Our results show that XGB generated the highest overall accuracy (OA) (95 ± 4%), followed by RF (92 ± 5%), ET (87 ± 6%), and TWDTW (81 ± 8%), for mapping four-classes of cropping intensity, with the near-infrared (NIR) band being the most important variable for identifying cropping intensity. This study demonstrates the potential of PlanetScope data for the production of cropping intensity maps at detailed resolutions.

Return to Work After Pectoralis Major Repair

Background: Pectoralis major repair (PMR) is an infrequent injury that occurs during resistance training, most commonly during the eccentric phase of muscle contraction. As the incidence of weight training continues to increase, it is important to understand the outcomes after PMR. Purpose: To evaluate the rate and duration of return to work in patients undergoing PMR. Study Design: Case series; Level of evidence, 4. Methods: Consecutive patients undergoing PMR from 2010 to 2016 at a single institution were retrospectively reviewed at a minimum of 1 year postoperatively. Patients completed a standardized and validated work questionnaire, as well as a visual analog scale for pain, American Shoulder and Elbow Surgeons survey, Single Assessment Numerical Evaluation, and a satisfaction survey. Results: Of the 60 eligible patients who had a PMR, 49 (81.7%) were contacted at the final follow-up. Of the 49 patients, 46 (93.9%) had been employed within 3 years before surgery (mean ± SD age, 40.4 ± 8.2 years; follow-up, 3.9 ± 2.8 years). Of these, 45 (97.8%) returned to work by 1.6 ± 2.1 months postoperatively, and 41 (89.1%) returned to the same level of occupational intensity. Patients who held sedentary, light-, medium-, or high-intensity occupations returned to work at a rate of 100.0%, 100.0%, 83.3%, and 66.7% by 0.8 ± 1.0, 0.8 ± 1.0, 1.3 ± 2.7, and 3.3 ± 2.7 months, respectively. Five of 6 patients (83.3%) with workers’ compensation returned to their previous occupations by 5.0 ± 1.6 months, while 100% of those without workers’ compensation returned to work by 1.1 ± 1.7 months ( P < .001). Overall, 44 patients (95.7%) were satisfied with the procedure, and 40 (87.0%) would have the operation again if presented the opportunity. A single patient (2.2%) required revision PMR. Conclusion: Approximately 98% of patients who underwent PMR returned to work by 1.6 ± 2.1 months postoperatively. Patients with higher-intensity occupations took longer to return to their preoperative levels of occupational intensity. Information regarding return to work is imperative in preoperative patient consultation to manage expectations.

A Probabilistic Approach For Registration Of Multi-Modal Spatial Transcriptomics Data

Observing the spatial characteristics of gene expression by image-based spatial transcriptomics technology allows studying gene activities across different cells and intracellular structures. We introduce a probabilistic approach for the registration and analysis of transcriptome images and immunostaining images. The method is based on particle filters, exploits both intensity information and image features, and takes into account multi-channel information. We applied our approach to two-channel synthetic data as well as real transcriptome images and immunostaining microscopy images of the mouse brain. It turns out that our approach accurately registers the multi-modal images and yields better results than a state-of-the-art method.

Vessel Target Detection in Spaceborne–Airborne Collaborative SAR Images via Proposal and Polarization Fusion

This paper focuses on vessel detection through the fusion of synthetic aperture radar (SAR) images acquired from spaceborne–airborne collaborative observations. The vessel target detection task becomes more challenging when it features inshore interferences and structured and shaped targets. We propose a new method, based on target proposal and polarization information exploitation (TPPIE), to fuse the spaceborne–airborne collaborative SAR images for accurate vessel detection. First, a new triple-state proposal matrix (TSPM) is generated by combining the normed gradient-based target proposal and the edge-based morphological candidate map. The TSPM can be used to extract the potential target regions, as well as filtering out the sea clutter and inshore interference regions. Second, we present a new polarization feature, named the absolute polarization ratio (APR), to exploit the intensity information of dual-polarization SAR images. In the APR map, the vessel target regions are further enhanced. Third, the final fused image with enhanced targets and suppressed backgrounds (i.e., improved target-to-clutter ratio; TCR) is attained by taking the Hadamard product of the intersected TSPM from multiple sources and the composite map exploiting the APR feature. Experimental analyses using Gaofen-3 satellite and unmanned aerial vehicle (UAV) SAR imagery indicate that the proposed TPPIE fusion method can yield higher TCRs for fused images and better detection performance for vessel targets, compared to commonly used image fusion approaches.

Sub-diffraction error mapping for localisation microscopy images

Assessing the quality of localisation microscopy images is highly challenging due to the difficulty in reliably detecting errors in experimental data. The most common failure modes are the biases and errors produced by the localisation algorithm when there is emitter overlap. Also known as the high density or crowded field condition, significant emitter overlap is normally unavoidable in live cell imaging. Here we use Haar wavelet kernel analysis (HAWK), a localisation microscopy data analysis method which is known to produce results without bias, to generate a reference image. This enables mapping and quantification of reconstruction bias and artefacts common in all but low emitter density data. By avoiding comparisons involving intensity information, we can map structural artefacts in a way that is not adversely influenced by nonlinearity in the localisation algorithm. The HAWK Method for the Assessment of Nanoscopy (HAWKMAN) is a general approach which allows for the reliability of localisation information to be assessed.

Unified ensemble mean forecasting of tropical cyclones based on the feature-oriented mean method

Operational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged.We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

Mathematical framework for place coding in the auditory system

In the auditory system, tonotopy is postulated to be the substrate for a place code, where sound frequency is encoded by the location of the neurons that fire during the stimulus. Though conceptually simple, the computations that allow for the representation of intensity and complex sounds are poorly understood. Here, a mathematical framework is developed in order to define clearly the conditions that support a place code. To accommodate both frequency and intensity information, the neural network is described as a space with elements that represent individual neurons and clusters of neurons. A mapping is then constructed from acoustic space to neural space so that frequency and intensity are encoded, respectively, by the location and size of the clusters. Algebraic operations -addition and multiplication- are derived to elucidate the rules for representing, assembling, and modulating multi-frequency sound in networks. The resulting outcomes of these operations are consistent with network simulations as well as with electrophysiological and psychophysical data. The analyses show how both frequency and intensity can be encoded with a purely place code, without the need for rate or temporal coding schemes. The algebraic operations are used to describe loudness summation and suggest a mechanism for the critical band. The mathematical approach complements experimental and computational approaches and provides a foundation for interpreting data and constructing models.

Extended set of superpixel features

Superpixel-based image processing and analysis methods usually use a small set of superpixel features. Expanding the description of superpixels can improve the quality of processing algorithms. In the paper, a set of 25 basic superpixel features of shape, intensity, geometry, and location is proposed. The features meet the requirements of low computational complexity in the process of image superpixel segmentation and sufficiency for solving a wide class of application tasks. Applying the set, we present a modification of the well-known approach to the superpixel generation. It consists of fast primary superpixel segmentation of the image with a strict homogeneity predicate, which provides superpixels preserving the intensity information of the original image with high accuracy, and the subsequent enlargement of the superpixels with softer homogeneity predicates. The experiments show that the approach can significantly reduce the number of image elements, which helps to reduce the complexity of processing algorithms, meanwhile the expanded superpixels more accurately correspond to the image objects.

Effects of Interactivity on Consumer Behavioral Intention in Mobile Social Commerce in China

The authors study the effects of perceived interactivity on consumer behavioral intention according to the characteristics of consumer behavior in mobile social commerce. Results indicate that the intensity of information interaction has significant and positive effects on the depth, breadth, and novelty of consumer recommendations and reviews. The intensity of interpersonal interaction has significant and positive effects on community-based trust and response intensity. Information interaction has significant and positive effects on interpersonal interaction and suggestion adoption. Findings also reveal the relationship between interactivity and consumer behavioral intention in mobile social commerce. The conclusions provide theoretical support for studying how these constructs interact with each other.