Description: Low-cost unmanned airborne vehicles (UAVs) are emerging as a promising platform for remote-sensing data acquisition to satisfy the needs of wide range of applications. Utilizing UAVs, which are equipped with directly georeferenced RGB-frame cameras and hyperspectral push-broom scanners, for precision agriculture and high-throughput phenotyping is an important application that is gaining significant attention from researchers in the mapping and plant science fields. The advantages of UAVs as mobile-mapping platforms include low cost, ease of storage and deployment, ability to fly lower and collect high-resolution data, and filling an important gap between wheel-based and manned-airborne platforms. However, limited endurance and payload are the main disadvantages of consumer-grade UAVs. These limitations lead to the adoption of low-quality direct georeferencing and imaging systems, which in turn will impact the quality of the delivered products. Thanks to recent advances in sensor calibration and automated triangulation, accurate mapping using low-cost frame imaging systems equipped with consumer-grade georeferencing units is feasible. Unfortunately, the quality of derived geospatial information from push-broom scanners is quite sensitive to the performance of the implemented direct georeferencing unit. This paper presents an approach for improving the orthorectification of hyperspectral push-broom scanner imagery with the help of generated orthophotos from frame cameras using tie point and linear features, while modeling the impact of residual artifacts in the direct georeferencing information. The performance of the proposed approach has been verified through real datasets that have been collected by quadcopter and fixed-wing UAVs over an agricultural field.

Description: Segmentation is a key issue in the processing of multidimensional images such as those in the field of remote sensing. Most of the segmentation algorithms developed for multidimensional images begin by reducing the dimensionality of the images, thus loosing information that could be relevant in the segmentation process. Evolutionary cellular automata segmentation (ECAS-II) is an evolutionary approach that provides cellular automata-based segmenters considering all the spectral information contained in a hyperspectral image without applying any technique for dimensionality reduction. This paper presents an efficient graphics processor unit implementation of the type of segmenters produced by ECAS-II for land cover hyperspectral images. The method is evaluated over remote sensing hyperspectral images, introducing it on a spectral–spatial classification scheme based on extreme learning machines. Experiments have shown that the proposed approach achieves better accuracy results for land cover purposes than other spectral–spatial classification techniques based on segmentation.

Description: Recent developments in sensor technology are contributing toward the tremendous growth of remote sensing (RS) archives (currently, at the petabyte scale). However, this data largely remain unexploited due to the current limitations in the data discovery, querying, and retrieval capabilities. This issue becomes exacerbated in disaster situations, where there is a need for rapid processing and retrieval of the affected areas. Furthermore, the retrieval of images based on the spatial configurations of affected regions [land use/cover (LULC) classes] in an image is important in disaster situations such as floods and earthquakes. The majority of existing Earth observation (EO) image information mining (IIM) systems does not consider the spatial relations among image regions during image retrieval (aka spatial semantic gap). In this work, we have specifically addressed two issues, i.e., explicit modeling of topological and directional relationships between image regions, and development of a resource description framework (RDF)-based spatial semantic graphs (SSGs). This enables more intuitive querying and reasoning on the archived data. A spatial IIM (SIIM) framework is proposed, which integrates a logic-based reasoning mechanism to extract the hidden spatial relationships (both topological and directional) and enables image retrieval based on spatial relationships. The system is tested using several spatial relations-based queries on the RS image repository of flood-affected areas to check its applicability in post flood scenario. Precision, recall, and F-measure metrics were used to evaluate the performance of the SIIM system, which showed good potential for spatial relations-based image retrieval.

Description: MapReduce has been widely used in Hadoop for parallel processing larger-scale data for the last decade. However, remote-sensing (RS) algorithms based on the programming model are trapped in dense disk I/O operations and unconstrained network communication, and thus inappropriate for timely processing and analyzing massive, heterogeneous RS data. In this paper, a novel in-memory computing framework called Apache Spark (Spark) is introduced. Through its merits of transferring transformation to in-memory datasets of Spark, the shortages are eliminated. To facilitate implementation and assure high performance of Spark-based algorithms in a complex cloud computing environment, a strip-oriented parallel programming model is proposed. By incorporating strips of RS data with resilient distributed datasets (RDDs) of Spark, all-level parallel RS algorithms can be easily expressed with coarse-grained transformation primitives and BitTorrent-enabled broadcast variables. Additionally, a generic image partition method for Spark-based RS algorithms to efficiently generate differentiable key/value strips from a Hadoop distributed file system (HDFS) is implemented for concealing the heterogeneousness of RS data. Data-intensive multitasking algorithms and iteration-intensive algorithms were evaluated on a Hadoop yet another resource negotiator (YARN) platform. Experiments indicated that our Spark-based parallel algorithms are of great efficiency, a multitasking algorithm took less than 4 h to process more than half a terabyte of RS data on a small YARN cluster, and 9*9 convolution operations against a 909-MB image took less than 260 s. Further, the efficiency of iteration-intensive algorithms is insensitive to image size.

Description: Open ocean and coastal area monitoring requires multispectral satellite images with a middle spatial resolution $({sim 300 {text{m}}})$ and a high temporal repeatability $({sim 1 {text{h}}})$ . As no current satellite sensors have such features, the aim of this study is to propose a fusion method to merge images delivered by a low earth orbit (LEO) sensor with images delivered by a geostationary earth orbit (GEO) sensor. This fusion method, called spatial spectral temporal fusion (SSTF), is applied to the future sensors—Ocean and Land Color Instrument (OLCI) (on Sentinel-3) and Flexible Combined Imager (FCI) (on Meteosat Third Generation) whose images were simulated. The OLCI bands, acquired at t 0 , are divided by the oversampled corresponding FCI band acquired at t 0 and multiplied by the FCI bands acquired at t 1 . The fusion product is used for the next fusion at t 1 and so on. The high temporal resolution of FCI allows its signal-to-noise ratio (SNR) to be enhanced by the means of temporal filtering. The fusion quality indicator ERGAS computed between SSTF fusion products and reference images is around 0.75, once the FCI images are filtered from the noise and 1.08 before filtering. We also compared the estimation of chlorophyll (Chl ) , suspended particulate matter (SPM), and colored dissolved organic matter (CDOM) maps from the fusion products with the input simulation maps. The comparison shows an average relative error s on Chl, SPM, and CDOM, respectively, of 64.6%, 6.2%, and 9.5% with the SSTF method. The SSTF method was also compared with an existing fusion method called the spati- l and temporal adaptive reflectance fusion model (STARFM).

Description: Accurately assessing the heavy-metal contamination in crops is crucial to food security. This study provides a method to distinguish heavy-metal stress levels in rice using the variations of two physiological functions as discrimination indices, which are obtained by assimilation of remotely sensed data with a crop growth model. Two stress indices, which correspond to daily total $text{CO}_{2}$ assimilation and dry-matter conversion coefficient were incorporated into the World Food Study (WOFOST) crop growth model and calculated by assimilating the model with leaf area index (LAI), which was derived from time-series HJ1-CCD data. The stress levels are not constant with rice growth; thus, to improve the reliability, the two stress indices were obtained at both the first and the latter half periods of rice growth. To compare the stress indices of different stress levels, a synthetic stress index was established by combining the two indices; then, three types of stress index discriminant spaces based on the synthetic index of different growth periods were constructed, in which the two-dimensional discriminant space based on two growth periods showed the highest accuracy, with a misjudgment rate of 4.5%. When the discrimination rules were applied at a regional scale, the average correct discrimination rate was 95.0%.

Description: The soil moisture active passive (SMAP) L-band synthetic aperture radar (SAR) could continuously provide global km scale ocean surface wind observations, which had a better coverage than other SARs and a higher spatial resolution than scatterometers. This paper investigates SMAP normalized radar cross sections (NRCS) dependence on wind vectors using more than 5 million matchups consisting of Defense Meteorological Satellite Program F17 Special Sensor Microwave Image/Sounder wind speed, National Center for Environmental Predication wind direction and SMAP L-band NRCS. An L-band geophysical model function (GMF) is proposed for SMAP wind retrieval on the basis of these matchups, and it indicates wind speed and direction dependence of SMAP L-band NRCS for about 40° incidence angle and 0–25 m/s wind speed range in both HH and VV polarization. The wind speed dependence increases rapidly with wind speed, and HH-polarized one is greater than VV polarization. The upwind–downwind difference for HH polarization is greater than that for VV polarization. A negative upwind–crosswind asymmetry occurs for HH- and VV-polarized backscatter at lower wind speeds. The retrieved SMAP wind speed using the proposed GMF is validated by using National Data Buoy Center buoy winds. The root mean square differences and biases are 1.77 and 0.19 m/s, respectively. The accuracies of SMAP wind speeds at 0–10 m/s range are better than those at higher wind speed range. In addition, SMAP wind speeds in upwind and downwind directions are relatively more accurate than those in crosswind directions.