The acquisition of such agricultural information as crop growth and output is of great significance for the development of modern agriculture. Recently, the techniques of remote sensing (RS) and geographic information system (GIS) have been widely used to estimate the crop yield and, as a result, a set of practical yield estimation methods are put forward. The yield estimation methods mainly include the yield estimation method combined with relative secondary data, the yield estimation method based on vegetation index, the yield estimation method based on the specific models, and the development of crop yield estimation platform (software). Among these means, the yield estimation method based on vegetation index is divided into two categories, i.e., the single vegetation yield estimation method and the multiple vegetation index yield estimation method. A few crop yield estimation methods are analyzed in this paper based on studying many recently published papers in this field, and the advantages and disadvantages of each method are reviewed. In addition, the orientations for further research in this field are discussed and forecast so as to provide some valuable references for researchers in this field.
Traditional hyperspectral image band grouping and reordering algorithms based on Prim require calculating the correlation coefficients between all bands, and full rank correlation coefficient matrix is used as the adjacent matrix for comparison, which causes high computational complexity. Combining the similarity measurement of fuzzy mathematics theory with the characteristics of the hyperspectral image, the maximum and minimum closeness(MMC)which possesses the characteristics of less computation is used as a parameter for measuring the correlation of the hyperspectral image bands. Then the adjacent matrix of MMC is processed into a sparse matrix and the effective bands is extracted for reordering. In this way, the number of bands used for ordering and the required times for band comparison will be significantly reduced. Experimental results show that, compared with the traditional Prim algorithm, the proposed algorithm greatly reduces the calculation complexity of the hyperspectral image band ordering while maintaining compression efficiency, and the average running time for band ordering has been reduced by 27%.
Owing to relatively serious irregular geometric deformation of the whole image, it is difficult to simulate the deformation property of the whole large HJ satellite image by using the global model. Neither the global nor the local calibration model based on traditional manual selection of the ground control point(GCP)is suitable for geometric accurate rectification of the large HJ satellite image, and hence the authors proposed an optimization local model of geometric rectification method for calibrating HJ satellite remote sensing image after selecting GCPs with the algorithm of features from accelerated segment test(FAST). This method can be described as follows: First, plenty of GCPs are automatically obtained based on FAST algorithm; Then, a polynomial model is employed to analyze and evaluate the points' matching ratio between non-matching GCPs and actual GCPs in the RMSE threshold with the correlation analysis method; In addition, the total error GCPs' numbers are calculated according to the analysis of the results and the choice of appropriate root-mean-square error threshold to eliminate error GCPs; Finally, the rectification is completed with local model of Linear Rubber Sheeting. Besides, the authors have established a criteria system which is suitable for the registration result by evaluating HJ satellite remote sensing image, including residuals scatter plot, spatial interpolation method and so on. The evaluation results show that the error of calibrated HJ satellite remote sensing image can be restricted within 1.5 pixels, and the calibrated image meets the requirements of mid-resolution's application satisfactorily.
From the shadow processing of high-resolution aerial remote sensing images, this paper analyzed the features of shadow in color space. Using combined thresholds of 3 channels in HIS color space and Gaussian function, the authors detected shadow area and its multi-scale geometric details which can compensate shadow area. The experiments prove that this method can maximize the retention of the original features in shadow area and get more reasonable compensation results, thus ensuring accuracy and reliability of the follow-up imaging.
Extracting road edge information from remote sensing image can simplify the conventional urban road mapping work. Based on the general road image features, this paper proposes a dual-threshold SSDA (sequential similarity detection algorithm) template matching method in an image processing model. And on the basis of the general SSDA, another algorithm is presented to reduce the excessive number of samples responsible for the error growth. Compared with other algorithms, this algorithm can more effectively access the road edge information extraction. As for some parts of the road which cannot be completely extracted through pretreatment process, the detection results can be corrected to reduce treatment, and hence the processing efficiency will be improved.
To solve the change detection problem of multi-channel remote sensing images, this paper proposes a method based on iterative estimation with weight selection (IEWS) and unsupervised classification (UC). Firstly, the primary change information is obtained according to the concept of IEWS, and the iteration scheme of the estimation is also similar to that of the iteratively re-weighted multivariate alteration detection (IRMAD). And then, the primary change information is classified by the UC and processed by the IEWS, which can get the eventual change information. The experimental results with multi-spectral data indicate that the method proposed in this paper is effective. By using this method, the spatial coherence between the change information and the change of land use/cover in this area is good. As for the detection of change in small regions, the method is especially obviouely better than the commonly-used methods of multivariate alteration detection (MAD) and IRMAD.
This paper presents a model for extracting water from remote sensing by using empirical mode decomposition(EMD)and fractal theory. The authors tried to improve accuracy with spectral information and texture characteristics. Principal component analysis was carried out on the image to obtain the biggest first principal component that contains effective information, then the fractal dimension of each pixel was calculated; at the same time, the first principal component was decomposed with the method of EMD to get the first three empirical mode functions, which, coupled with the original band information, served as the research data. With the method of maximum likelihood classifier, the waters were extracted. This method fully combines the advantages of EMD method in noise reduction and the advantage of fractal theory in texture information extraction. Experiment shows that this method can effectively improve the extraction accuracy, with the Kappa up to 0.932 5.
The development of high spatial resolution remote sensing makes it possible to describe the urban fringe landscape pattern at the small scales by using remote sensing images. In this study, focusing on the method for integrating describing landscape patch mosaic, continuum and connectivity characteristics, the authors tried to set up a Delaunay-Voronoi hybrid landscape model based on high spatial resolution imagery object-based analysis. This method was also compared with the traditional pixel aggregation method. The result shows that the image segmentation method can well preserve the tiny critical landscape characteristics and postpone their disappearance during the pushing-up of the scale, and that Delaunay-Voronoi-based data structure is suitable for representing the hybrid landscape model and its multi-scale analysis.
The classification accuracy of superpixel-based conditional random fields(CRFs) model greatly depends on segmentation scale parameters, which constitutes a problem that should be solved. Therefore, to answer the question "whether a pixel-based CRFs model performs well in HSR image classification with m level spatial resolution or not",the authors proposed a pixel-based CRFs model with the association term defined as an output of random forests classifier and the interaction potential defined as Potts function weighted by contrast function, and the definition of association and interaction terms adopted multi-cue features such as histogram of gradient, multi- scale and multi-direction Texton filter and multi-spectral information from HSR imagery. Finally, the proposed model was trained using piecewise training method and inferred using α-expansion algorithm based on graph cut. Experiments on a typical urban scene from QuickBird multi-spectral satellite imagery have shown that the proposed RF-CRFs model shows the classification accuracy of over 82.52%. In addition, the classification accuracy of the model is higher than that of the RF classifier by 3.35% on average.
Based on mini-tree models, the authors constructed forest stands with different distributions to measure bidirectional reflectance factor (BRF) by a mini goniometer. The results show that the observed BRF of simulated forest canopies is reasonable and inter-compared with a three-dimensional BRF model. The reflectance in different view directions indicates a typical "bowl" shape in the near-infrared band, with significant "hot spot" effects in solar principal plane. The study has confirmed that mini-tree models can be applied to the research on forest BRF. The comparisons between different spatial distributions and terrain conditions of tree models demonstrate that forest density and slope have certain effects on forest BRF. Therefore, their features and regularity can contribute to the inversion of the land surface parameters through modeling.
ZY-3 and Landsat8 are new satellites lunched recently. In terms of the two kinds of images acquired by the two satellites, the applicability evaluation of the common fusion methods is insufficient. In this paper, the adaptability evaluation of the 6 fusion methods including wavelet transform(WT), Gram_Schimdt transform(G-S), principal component analysis (PCA), Pansharp and HIS for ZY-3 and Landsat8 image fusion was discussed, and the spectral information fidelity and spatial information integration were used to evaluate the quality of image fusion. The results of quality evaluation show that, in terms of spatial information integration, IHS transform is the best, followed by PCA, Brovey, G-S and WT, and Pansharp is the worst transform for ZY-3 image; G-S transform is the best, and Pansharp is the worst transform for Landsat8 image. Nevertheless, in terms of spectral information fidelity, PCA transform is the best, followed by IHS, G-S and Brovey, and WT is the worst transform for ZY-3 image, G-S transform is the best, followed by Pansharp and Brovey, and IHS, WT and PCA are worse transforms for Landsat8 image.
SPOT6 is a new remote sensing satellite launched in 2012,with the characteristics of high spatial resolution and strong acquisition capability. However, a complete data preprocessing technology for the regulation of land resources has not yet been formed. According to the characteristics of SPOT6 satellite images, four different image fusion methods of Gram-Schmidt, HPF, Pansharp and PanSharpening were selected to conduct the experiment of comparison by using the software platforms of ENVI, ERDAS and PCI. For evaluating the results' performances, the authors compared them in three aspects. The image quality of experiment results was evaluated qualitatively and also assessed quantitatively by establishing evaluation indexes including mean,standard deviation,information entropy,average gradient and correlation coefficient. The application result of fused images was evaluated based on the evaluation of the classification accuracy. The analytical results show that these algorithms would work in different ways and could be used in different applications. The results achieved by the authors can provide the technical support for application of SPOT6 image to the land resources management.
This paper introduces a program called landsat ecosystem disturbance adaptive processing system (LEDAPS) for the image stacks creation of the atmospherically corrected Landsat dense time series standard products from 1987 to 2011. Landsat images were first calibrated to top-of-atmosphere (TOA) reflectance by using solar zenith, Sun-Earth distance, TM or ETM+ bandpass, and solar irradiance (using the MODTRAN solar output model). The interpolated aerosol optical thickness (AOT) which was interpolated spatially between the "dark dense vegetation (DDV)" using a spline algorithm, ozone, atmospheric pressure, and water vapor were supplied to the 6S radioactive transfer algorithm to convert TOA reflectance into ground surface reflectance for each 30 m pixel. The algorithm was applied to the LEDAPS standard data of Landsat7 ETM+ and non-standard data of Landsat5 TM to illustrate the data choice, data format unification and the algorithm implementation of the dense Landsat time series. Finally, a method for the validation of the corrected images was provided. The results show that the surface reflectance products resulting from the LEDAPS processing could effectively reduce the influence caused by ozone, water vapor, and aerosol particles in the atmosphere on the true image surface reflectance. The surface reflectivity is more precise and provides standard products for multiple scientific applications, such as land cover change or forest disturbance dynamic characterization and remote sensing based biophysical parameters retrieval, thus beneficial to formulating criteria for processing sequence image data in China.
With the popularization of high resolution remote sensing data of domestic satellite in such fields as land resources, geology, and environment, domestic high-resolution remote sensing data are likely to become the preferred data source for monitoring the development status of mining areas in the near future. Using the ZY-1 02C and GF-1 images, the authors designed an integrated solution for geodatabase building of remote sensing images, image processing, geometric correction, information extraction, spatial calculation, statistical analysis, and final mapping production, and successfully applied such means to the related work in Tibet. The results achieved by the authors can increase the depth and scope of the application of the domestic satellite remote sensing data, and provide technical support and paradigms for large-scale and multi-period dynamic remote sensing monitoring of mines.
At present, the aerial photographic quality inspection still uses the traditional method to check the digital frame images' quality, which prints the digital images on paper first and then checks them manually. The method greatly limits the advantages of digital aerial photography. This paper presents a fully digital aerial photographic quality inspection method, whose whole process is fully completed on computer. Firstly, index images are used to complete the image quality inspection, Then the aerial photography flight quality inspection software and airborne POS (position and orientation system) data are employed to check such quality factors as overlap, swing angle, strip deformation, and flying height, Finally, relevant documentation is collated and prepared according to the results. In order to verify the reliability and scientificalness of this method, the authors selected the manual quality inspection results of Hengyang area in Hunan Province to make comparison and analysis. The results show that this method can actually reflect the flight quality of aerial photography, is in good consistency with manual results, and has some other advantages such as simple operation, high efficiency and low cost.
Multi-track InSAR measurements provide the potential for 2D and 3D displacement field retrieval and are commonly applied to coseismic deformation and earthquake source parameters estimation. Actually it is not possible to estimate 3D displacement vector since we can't generate 3D observations for the same area. Joint analysis of ascending and descending PSInSAR data enables the retrieval of vertical and horizontal displacement according to the sensitivity of InSAR measurements in different directions. In this paper, the authors made an experimental study of vertical and horizontal displacement retrieval by joint analysis of ascending and descending PSInSAR data. A novel model has been adopted to estimate vertical and east-west displacement with the ascending and descending data. The retrieval displacement indicates that the difference between ascending and descending PSInSAR measurements is not clear and the single track PSInSAR can be well applied to subsidence monitoring.
An open technological platform can promote public participation in reasonable formulation and implementation of land use master planning. Based on GIS, VR, and touch screen, the authors explored an open display platform for land use master planning which can popularize professional contents to the public in an imaging and intuitive form. Studies show that innovative integration of cross-domain technology is feasible. The coupling of 2-3D GIS with virtual reality can create a simulating scene of land use master planning that presents pictures, texts, figures, sounds simultaneously. This platform can break through the limitations of traditional paper posting, realize open planning, and promote the public service function of the government.