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  • Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-11-13
    A. de la Casa, G. Ovando, L. Bressanini, J. Martínez, G. Díaz, C. Miranda

    To explore precision farming profits, the variability within a plot can be evaluated using digital technology by different remote means. The objectives of this study were to determine crop coverage (CC) of soybean (Glycine max (L.) Merril) with Normalized Difference Vegetation Index (NDVI) data obtained by digital photographs on the field and from the satellites LANDSAT (7 and 8), with an overpass each 16 days and a pixel of 30 m, and PROBA-V, which has daily frequency and 100 m of spatial resolution, in order to evaluate productivity differences between sectors of a 45 ha rainfed plot located at south of Córdoba city, Argentina. In the plot, sowed on 22/11/2014 and harvested on 10/04/2015, 16 sampling areas were established to record periodically photographs with a modified camera and, in 8 of them, supplementary crop information. A non-linear model was developed from NDVI data of digital camera (NDVIC) to estimate the soybean CC that showed an appropriate predictive performance. Furthermore, NDVI data of LANDSAT (7 and 8) (NDVIL) and PROBA-V (NDVIP-V) were also applied to estimate CC, resulting in models whose structure and accuracy was similar to that obtained with the digital camera (R2 = 0.956 and 0.939, respectively). According to the radiometric information the two instruments provide, the digital images classification procedure to determine CC requires increasing the threshold from 0.0 to 0.05 when soybean progresses towards the maturation and senescence stages and green material is mixed with the senescent one. Growing conditions were very favorable for soybean in 2014–2015, since precipitation (PP) not only showed a marked continuity with 60 rainy days during the cycle, but also 642 mm accumulated in this period far exceeded maximum evapotranspiration (ETmax) of 389 mm. The CC had a major development in all sectors, maintaining a complete coverage condition for more than 50 days during most of the reproductive stage. However, prevalent overcast sky restricted significantly solar radiation (SR) and reduced potential yield (PY) to an average value close to 6000 kg ha−1 which, according to the plot yield map, produced a reduced yield gap (YG) between 10.6 and 19.8%. From the proposed model and with the NDVI data of LANDSAT 7 (NDVI7), soybean CC was estimated in the same plot for 2010–2011. Water availability were less favorable in this case, with accumulated values of 584 mm and 460 mm, for PP and ETmax, respectively, while a higher availability of SR during the crop season increased notably PY that reached a range between 7347 and 8224 kg ha−1. Moreover, lower water availability was evidenced increasing YG in the plot (40–53%). From the spatial evaluation carried out, only one-third of the plot located at the south reached the highest productivity in both crop seasons, leaving open the question about the weather influence in each productive cycle with respect to the effectiveness of the site-specific management.

    更新日期:2018-11-13
  • Successional stages and their evolution in tropical forests using multi-temporal photogrammetric surface models and superpixels
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-11-09
    Adilson Berveglieri, Nilton N. Imai, Antonio M.G. Tommaselli, Baltazar Casagrande, Eija Honkavaara

    Airborne photogrammetric image archives offer interesting possibilities for multi-temporal analyses of environmental evolution. The objective of this investigation was to develop a technique for classifying forest successional stages and performing multi-temporal analyses of the tree canopy based on tree height variances calculated from digital surface models (DSMs) created from photogrammetric imagery. Furthermore, our objective was to evaluate the usability of the technique in assessing the evolution of successional stages in a tropical forest. The local variance calculation in 3D space resulted in an image that was subdivided with a segmentation technique to generate small areas called superpixels. These superpixels, which use the local mean variance as an attribute, are assessed via cluster analysis to evaluate statistical similarity and define successional stage classes. The same superpixel shapes were located in georeferenced historical datasets to enable multi-temporal analysis. The cluster analysis of temporal superpixels enabled the spatiotemporal classification of forest canopy evolution. The technique was used to assess a tropical forest remnant in Brazil. Dense DSMs were generated with stereo-photogrammetric techniques using optical images (both film and digital images) from which height variances were computed. A cluster analysis of superpixels was performed to classify the forest canopy into four successional stages, which were consistent with Brazilian classification rules. The multi-temporal analysis identified six classes of forest cover evolution. Field data were collected in forest plots to validate the generated forest canopy classifications. The results showed that the proposed approach was feasible for forest cover classification and for identifying changes in the vertical forest structure and cover over time using only optical images.

    更新日期:2018-11-10
  • Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-10-08
    Fabien Hubert Wagner, Matheus Pinheiro Ferreira, Alber Sanchez, Mayumi C.M. Hirye, Maciel Zortea, Emanuel Gloor, Oliver L. Phillips, Carlos Roberto de Souza Filho, Yosio Edemir Shimabukuro, Luiz E.O.C. Aragão
    更新日期:2018-10-08
  • Satellite-based view of the aerosol spatial and temporal variability in the Córdoba region (Argentina) using over ten years of high-resolution data
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-09-26
    Lara Sofía Della Ceca, María Fernanda García Ferreyra, Alexei Lyapustin, Alexandra Chudnovsky, Lidia Otero, Hebe Carreras, Francesca Barnaba

    Space-based observations offer a unique opportunity to investigate the atmosphere and its changes over decadal time scales, particularly in regions lacking in situ and/or ground based observations. In this study, we investigate temporal and spatial variability of atmospheric particulate matter (aerosol) over the urban area of Córdoba (central Argentina) using over ten years (2003–2015) of high-resolution (1 km) satellite-based retrievals of aerosol optical depth (AOD). This fine resolution is achieved exploiting the capabilities of a recently developed inversion algorithm (Multiangle implementation of atmospheric correction, MAIAC) applied to the MODIS sensor datasets of the NASA-Terra and -Aqua platforms. Results of this investigation show a clear seasonality of AOD over the investigated area. This is found to be shaped by an intricate superposition of aerosol sources, acting over different spatial scales and affecting the region with different yearly cycles. During late winter and spring (August-October), local as well as near- and long-range transported biomass burning (BB) aerosols enhance the Córdoba aerosol load, and AOD levels reach their maximum values (>0.35 at 0.47 µm). The fine AOD spatial resolution allowed to disclose that, in this period, AOD maxima are found in the rural/agricultural area around the city, reaching up to the city boundaries pinpointing that fires of local and near-range origin play a major role in the AOD enhancement. A reverse spatial AOD gradient is found from December to March, the urban area showing AODs 40–80% higher than in the city surroundings. In fact, during summer, the columnar aerosol load over the Córdoba region is dominated by local (urban and industrial) sources, likely coupled to secondary processes driven by enhanced radiation and mixing effects within a deeper planetary boundary layer (PBL). With the support of modelled AOD data from the Modern-Era Retrospective Analysis for Research and Application (MERRA), we further investigated into the chemical nature of AOD. The results suggest that mineral dust is also an important aerosol component in Córdoba, with maximum impact from November to February. The use of a long-term dataset finally allowed a preliminary assessment of AOD trends over the Córdoba region. For those months in which local sources and secondary processes were found to dominate the AOD (December to March), we found a positive AOD trend in the Córdoba outskirts, mainly in the areas with maximum urbanization/population growth over the investigated decade. Conversely, a negative AOD trend (up to −0.1 per decade) is observed all over the rural area of Córdoba during the BB season, this being attributed to a decrease of fires both at the local and the continental scale.

    更新日期:2018-09-26
  • Radargrammetric approaches to the flat relief of the amazon coast using COSMO-SkyMed and TerraSAR-X datasets
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-09-13
    Ulisses Silva Guimarães, Igor da Silva Narvaes, Maria de Lourdes Bueno Trindade Galo, Arnaldo de Queiroz da Silva, Paulo de Oliveira Camargo
    更新日期:2018-09-13
  • Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-05-08
    Xiang Gao, Shuhan Shen, Yang Zhou, Hainan Cui, Lingjie Zhu, Zhanyi Hu

    Ancient Chinese architecture 3D digitalization and documentation is a challenging task for the image based modeling community due to its architectural complexity and structural delicacy. Currently, an effective approach to ancient Chinese architecture 3D reconstruction is to merge the two point clouds, separately obtained from ground and aerial images by the SfM technique. There are two understanding issues should be specially addressed: (1) it is difficult to find the point matches between the images from different sources due to their remarkable variations in viewpoint and scale; (2) due to the inevitable drift phenomenon in any SfM reconstruction process, the resulting two point clouds are no longer strictly related by a single similarity transformation as it should be theoretically. To address these two issues, a new point cloud merging method is proposed in this work. Our method has the following characteristics: (1) the images are matched by leveraging sparse mesh based image synthesis; (2) the putative point matches are filtered by geometrical consistency check and geometrical model verification; and (3) the two point clouds are merged via bundle adjustment by linking the ground-to-aerial tracks. Extensive experiments show that our method outperforms many of the state-of-the-art approaches in terms of ground-to-aerial image matching and point cloud merging.

    更新日期:2018-05-08
  • Automatic 3D reconstruction of electrical substation scene from LiDAR point cloud
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-05-08
    Qiaoyun Wu, Hongbin Yang, Mingqiang Wei, Oussama Remil, Bo Wang, Jun Wang

    3D reconstruction of a large-scale electrical substation scene (ESS) is fundamental to navigation, information inquiry, and supervisory control of 3D scenes. However, automatic reconstruction of ESS from a raw LiDAR point cloud is challenging due to its incompleteness, noise and anisotropy in density. We propose an automatic and efficient approach to reconstruct ESSs, by mapping raw LiDAR data to our well-established electrical device database (EDD). We derive a flexible and hierarchical representation of the ESS automatically by exploring the internal topology of the corresponding LiDAR data, followed by extracting various devices from the ESS. For each device, a quality mesh model is retrieved in the EDD, based on the proposed object descriptor that can balance descriptiveness, robustness and efficiency. With the high-level representation of the ESS, we map all retrieved models into raw data to achieve a high-fidelity scene reconstruction. Extensive experiments on large and complex ESSs modeling demonstrate the efficiency and accuracy of the proposed method.

    更新日期:2018-05-08
  • Multi-scale object detection in remote sensing imagery with convolutional neural networks
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-05-02
    Zhipeng Deng, Hao Sun, Shilin Zhou, Juanping Zhao, Lin Lei, Huanxin Zou

    Automatic detection of multi-class objects in remote sensing images is a fundamental but challenging problem faced for remote sensing image analysis. Traditional methods are based on hand-crafted or shallow-learning-based features with limited representation power. Recently, deep learning algorithms, especially Faster region based convolutional neural networks (FRCN), has shown their much stronger detection power in computer vision field. However, several challenges limit the applications of FRCN in multi-class objects detection from remote sensing images: (1) Objects often appear at very different scales in remote sensing images, and FRCN with a fixed receptive field cannot match the scale variability of different objects; (2) Objects in large-scale remote sensing images are relatively small in size and densely peaked, and FRCN has poor localization performance with small objects; (3) Manual annotation is generally expensive and the available manual annotation of objects for training FRCN are not sufficient in number. To address these problems, this paper proposes a unified and effective method for simultaneously detecting multi-class objects in remote sensing images with large scales variability. Firstly, we redesign the feature extractor by adopting Concatenated ReLU and Inception module, which can increases the variety of receptive field size. Then, the detection is preformed by two sub-networks: a multi-scale object proposal network (MS-OPN) for object-like region generation from several intermediate layers, whose receptive fields match different object scales, and an accurate object detection network (AODN) for object detection based on fused feature maps, which combines several feature maps that enables small and densely packed objects to produce stronger response. For large-scale remote sensing images with limited manual annotations, we use cropped image blocks for training and augment them with re-scalings and rotations. The quantitative comparison results on the challenging NWPU VHR-10 data set, aircraft data set, Aerial-Vehicle data set and SAR-Ship data set show that our method is more accurate than existing algorithms and is effective for multi-modal remote sensing images.

    更新日期:2018-05-03
  • Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-04-30
    Ronald Kemker, Carl Salvaggio, Christopher Kanan

    Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semantic segmentation) thanks to a large repository of annotated image data. Large labeled datasets for other sensor modalities, e.g., multispectral imagery (MSI), are not available due to the large cost and manpower required. In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery. To overcome label scarcity for MSI data, we substitute real MSI for generated synthetic MSI in order to initialize a DCNN framework. We evaluate our network initialization scheme on the new RIT-18 dataset that we present in this paper. This dataset contains very-high resolution MSI collected by an unmanned aircraft system. The models initialized with synthetic imagery were less prone to over-fitting and provide a state-of-the-art baseline for future work.

    更新日期:2018-05-01
  • L0-regularization-based skeleton optimization from consecutive point sets of kinetic human body
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-05-01
    Yong Zhang, Bowei Shen, Shaofan Wang, Dehui Kong, Baocai Yin

    Human skeleton extraction is essential for shape abstraction, estimation and analysis. However, it is difficult to implement with the existence of sparse data or noise and the shortage of connectivity within point clouds. To tackle this problem, we propose L0-regularization-based skeleton optimization method from consecutive point sets of kinetic human body. We firstly give an initial reconstruction of a dense point cloud from multi-view human motion images, and extract L1L1-medial skeleton from each point set individually, and then partition all skeleton points into semantic components, from which the partitioned point set is then sampled into skeleton sequence. By further observing that consecutive frames reflecting same body actions may present similar moving trajectories, we build geometric correlations spatiotemporally between adjacent frames. To be specific, our method proposes a temporal constraint and a spatial constraint, where the first constraint considers not only the correlations between each frame and the others, but also the correlations between adjacent frames, and the second one depicts the correlation within the same skeleton block and within the joint points that between different blocks to prevent the non-equidistant distribution of the skeleton points. By integrating the above spatio-temporal constraints, we establish a sparse optimization model and apply L0 optimization to all point sets of different frames. Experimental results show that our method can recover missing skeleton points, correct outliers in skeletons and smooth skeletons in the process of movement while retaining the action features of these skeletons.

    更新日期:2018-05-01
  • Semantic line framework-based indoor building modeling using backpacked laser scanning point cloud
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-04-25
    Cheng Wang, Shiwei Hou, Chenglu Wen, Zheng Gong, Qing Li, Xiaotian Sun, Jonathan Li

    Indoor building models are essential in many indoor applications. These models are composed of the primitives of the buildings, such as the ceilings, floors, walls, windows, and doors, but not the movable objects in the indoor spaces, such as furniture. This paper presents, for indoor environments, a novel semantic line framework-based modeling building method using backpacked laser scanning point cloud data. The proposed method first semantically labels the raw point clouds into the walls, ceiling, floor, and other objects. Then line structures are extracted from the labeled points to achieve an initial description of the building line framework. To optimize the detected line structures caused by furniture occlusion, a conditional Generative Adversarial Nets (cGAN) deep learning model is constructed. The line framework optimization model includes structure completion, extrusion removal, and regularization. The result of optimization is also derived from a quality evaluation of the point cloud. Thus, the data collection and building model representation become a united task-driven loop. The proposed method eventually outputs a semantic line framework model and provides a layout for the interior of the building. Experiments show that the proposed method effectively extracts the line framework from different indoor scenes.

    更新日期:2018-04-26
  • Monitoring Andean high altitude wetlands in central Chile with seasonal optical data: A comparison between Worldview-2 and Sentinel-2 imagery
    ISPRS J. Photogramm. Remote Sens. (IF 5.994) Pub Date : 2018-04-13
    Rocío A. Araya-López, Javier Lopatin, Fabian E. Fassnacht, H. Jaime Hernández

    In the Maipo watershed, situated in central Chile, mining activities are impacting high altitude Andean wetlands through the consumption and exploitation of water and land. As wetlands are vulnerable and particularly susceptible to changes of water supply, alterations and modifications in the hydrological regime have direct effects on their ecophysiological condition and vegetation cover. The aim of this study was to evaluate the potential of Worldview-2 and Sentinel-2 sensors to identify and map Andean wetlands through the use of the one-class classifier Bias support vector machines (BSVM), and then to estimate soil moisture content of the identified wetlands during snow-free summer using partial least square regression.The results obtained in this research showed that the combination of remote sensing data and a small sample of ground reference measurements enables to map Andean high altitude wetlands with high accuracies. BSVM was capable to classify the meadow areas with an overall accuracy of over ∼78% for both sensors. Our results also indicate that it is feasible to map surface soil moisture with optical remote sensing data and simple regression approaches in the examined environment. Surface soil moisture estimates reached r2 values of up to 0.58, and normalized mean square errors of 19% using Sentinel-2 data, while Worldview-2 estimates resulted in non-satisfying results. The presented approach is particularly valuable for monitoring high-mountain wetland areas with limited accessibility such as in the Andes.

    更新日期:2018-04-25
Some contents have been Reproduced with permission of the American Chemical Society.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
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