HJ-CCD Images Research Articles

Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R 2 of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.

Optimal fractal band selection on HJ-1 CCD image for land use classification

环境一号卫星（HJ-1）CCD影像光谱波段较少，地物之间的准确分类识别有一定困难。采用分形纹理辅助地物分类识别是一种有效方法，而波段选择是提高分类识别精度的关键。本文以江西赣州定南县土地利用分类为例，采用双毯覆盖模型对HJ卫星CCD影像6类典型地物的波谱分形特征进行了分析，利用不同地物在不同波段上的分形区分度差异构建了最佳分形波段选择模型，并利用该模型挑选出最佳分形波段来辅助土地利用分类，最后对分类结果进行检验。结果表明：最佳分形波段选择模型能够综合权衡不同地物在不同波段上的分形区分度差异，利用挑选出来的最佳分形波段来辅助分类，其分类总体精度相对于原始影像分类提高了11.77%，相对于第1主成分分形辅助下的分类提高了1.56%。

FUSION OF OPTICAL DATA AND SAR DATA FOR THE ESTIMATION OF NITROGEN CONCENTRATION IN PEARL RIVER ESTUARY HONG KONG SEAS, CHINA

Abstract. The knowledge of nitrogen concentration in the ocean is fundamental for the study of oceanic biogeochemical processes. The objective of this research is to estimate total inorganic nitrogen (TIN) by integrating optical parameters from HJ-1 CCD image and polarization parameters from RADARSAT-2 quad-polarization image. The situ data and HJ-1 CCD, RADARSAT-2 image were acquired from Pearl River Estuary Hong Kong Seas, China in August, 2010. The four sensitive parameters, reflectance of Band 4, NDSI (Normalized Difference Spectral Index), the backscattering coefficient of HV and VH were derived as input variables to assess the TIN. A multiple regression model was established between four input variables and TIN. The result showed that the fusion of optical data and SAR data was proved to be successful in estimating TIN in sea surface, with the correlation coefficient (R2) between measured TIN and predicated TIN of 0.774, and the root mean square error (RMSE) of 0.063. The optical data in combination with SAR data is promising for detecting biochemical component in sea surface.

A fully automatic registration approach based on contour and SIFT for HJ-1 images

To achieve a fully automatic registration between HJ-1 CCD images and HJ-1 infrared images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives, and the local deformations within the images. In this paper, aimed at those registration issues, a fully automatic registration approach based on contour and SIFT is proposed. The registration technique performs a pre-registration process using contour feature matching algorithm that decides the overlapping region between a reference image and an input image. Once the coarse regions are obtained, it performs a fine registration process based on SIFT detector and a local adaptive matching strategy. In the fine registration process, image blocking theory is used, which not only speeds up the features extraction and matching, but also makes the matching point pairs distributed uniformly in images, and further improves the accuracy of input image rectification. Experiments with visible images and infrared images from HJ-1A/B demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote sensing images registration.

北部湾红树林的HJ 星多光谱遥感提取模式

Mangrove extraction in large areas using multispectral remote sensing can be expected to be affected by many ground objects and by self-coverage. To address this issue, we created a mangrove extraction mode for large coastal areas based HJ CCD images of Beibu Gulf, combining mangrove spatial distribution characteristics and spectral features. First, all image spectra of ground cover were analyzed and a sea-ratio index for instantaneous coast-line identification was implemented, and two additional mangrove feature indices were generated. The growth region was divided according to criteria of mangrove habitat environment limitations and reflectance changes depending on different mangrove coverage levels, then the final extraction mode was established using mangrove feature indices that corresponded to different NDVI coverage densities. Application of this mode showed that it was capable of producing precise results at different coverage levels, and that the detrimental influence of other factors could for the largest part be eliminated. An overall cartographic accuracy of 83.70% and a consumer precision of 79.73% were obtained, validating the feasibility of HJ CCD image applications in coastal mangrove research.

Object-oriented land cover classification using HJ-1 remote sensing imagery

The object-oriented information extraction technique was used to improve classification accuracy, and addressed the problem that HJ-1 CCD remote sensing images have only four spectral bands with moderate spatial resolution. We used two key techniques: the selection of optimum image segmentation scale and the development of an appropriate object-oriented information extraction strategy. With the principle of minimizing merge cost of merging neighboring pixels/objects, we used spatial autocorrelation index Moran’s I and the variance index to select the optimum segmentation scale. The Nearest Neighborhood (NN) classifier based on sampling and a knowledge-based fuzzy classifier were used in the object-oriented information extraction strategy. In this classification step, feature optimization was used to improve information extraction accuracy using reduced data dimension. These two techniques were applied to land cover information extraction for Shanghai city using a HJ-1 CCD image. Results indicate that the information extraction accuracy of the object-oriented method was much higher than that of the pixel-based method.

Analysis of consistency between HJ-CCD images and TM images in monitoring rice LAI

Analysis of consistency between HJ-CCD images and TM images in monitoring rice LAI