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1.
基于ETM＋制作土地利用覆盖图——以制作北京1：5万土地利用覆盖图为例 总被引：3，自引：1，他引：2
简要介绍了基于LANDSAT7 ETM＋影像,采用计算机非监督分类、监督分类与人工解译相结合的方法制作土地利用覆盖图的过程和所采用的关键技术,给出了适用于规模化生产土地利用覆盖数据的工艺流程图。使用该方法制作的十一种分类要素的北京地区1：5万土地利用覆盖图,平均分类精度为84．85％,可以满足一般用户对土地利用覆盖图的要求。 相似文献
2.
《ISPRS Journal of Photogrammetry and Remote Sensing》1999,54(4):254-262
The Canadian satellite RADARSAT launched in November 1995 acquires C-band HH polarisation Synthetic Aperture Radar (SAR) data in various incident angles and spatial resolutions. In this study, the Standard Beam S7 SAR data with 45°–49° incidence angle has been used to discriminate rice and potato crops grown in the Gangetic plains of West Bengal state. Four-date data acquired in the 24-day repeat cycle between January 2 and March 15, 1997 was used to study the temporal backscatter characteristics of these crops in relation to the growth stages. Two, three and four-date data were used to classify the crops. The results show that the backscatter was the lowest during puddling of rice fields and increased as the crop growth progressed. The backscatter during this period changed from −18 dB to −8 dB. This temporal behaviour was similar to that observed in case of ERS-SAR data. The classification accuracy of rice areas was 94% using four-date data. Two-date data, one corresponding to pre-field preparation and the other corresponding to transplantation stage, resulted in 92% accuracy. The last observation is of particular interest as one may estimate the crop area as early as within 20–30 days of transplantation. Such an early estimate is not feasible using optical remote sensing data or ERS-SAR data. The backscatter of potato crop varied from −9 dB to −6 dB during the growth phase and showed large variations during early vegetative stage. Two-date data, one acquired during 40–45 days of planting and another at maturing stage, resulted in 93% classification accuracy for potato. All other combinations of two-date data resulted in less than 90% classification accuracy for potato. 相似文献
3.
S. Panigrahy M. Chakraborty K. R. Manjunath N. Kundu J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2000,28(1):59-65
Radarsat ScanSAR Narrow (SN2) data acquired on July 24 and August 17, 1997 were used to analyse the signature of rice crop in West Bengal, India. The analysis showed that the lowland practice of cultivation gives a distinct signature to rice due to the initial water background. The relatively stable backscatter from water bodies in temporal data enhanced the separability of rice fields from water using two date data. Around 94 per cent classification accuracy was achieved for rice crop using two date data. It was feasible to discriminate rice sub-classes based on their planting period like early and late crop. The analysis indicates the suitability of ScanSAR data for large area rice crop monitoring as it has a wide swath of 300 km. 相似文献
4.
K. S. Rao Manisha G. Naidu Jyoti Sakalley Santosh Phalke H. K. Aljassar 《Journal of the Indian Society of Remote Sensing》2005,33(2):267-276
The DEM of the Bhuj earthquake affected area of 50 x 50 km was generated using the ERS-1/2 SAR tandem data (May 15—16,1996).
Region growing algorithm coupled with path following approach was used for phase unwrapping. Phase to height conversion was
done using D-GPS control points. Geocoding was done using GAMMA software. A sample data of DEM of Shuttle Radar Topography
Mission (SRTM) of the Bhuj area is made available by DLR Germany. The intensity image, DEM and Error map are well registered.
The spatial resolution of this DEM is about 25 m with height accuracy of a few meters. The DEM derived through ERS SAR data
is prone to atmospheric affects as the required two images are acquired in different timings where as SRTM acquired the two
images simultaneously. An RMS height error of 12.06 m is observed with reference to SRTM though some of the individual locations
differ by as much as 35 m. 相似文献
5.
S. Panigrahy V. Jain C. Patnaik J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2012,40(4):599-606
In-season rice area estimation using C-band Synthetic Aperture Radar (SAR) data from RADARSAT-1 is being done in India for more than a decade. Decision rule based models in backscatter domain have been calibrated and validated using extensive field data and a long term backscatter signature bank of rice fields has been developed. Since the rice crop growing environment in India is a diverse one in the world having all the rice cultural types, the rice backscatter is quite exhaustive. This paper highlights the results of classification of rice lands in Bangladesh using the signature bank of India. The results showed that the Aman rice crop of Bangladesh has a typical temporal backscatter of shallow and intermediate rice fields of that of West Bengal state. The mean backscatter of the intermediate/deep water fields in southern Bangladesh was ?19?dB, while that of shallow cultural types mostly in northern Bangladesh was ?17?dB. The signature of the rice crop in Southern Bangladesh matched well with that of Gangetic West Bengal, particularly that of the 24 Parganas, Howrah and Hughli districts. The signature of rice crop in the Sub-Himalayan West Bengal particularly that of Dinajpur and Maldah districts matched well with that of the northern area of Bangladesh. State level rice area estimated using the selected models was found with in 5% deviation from that of the reported acreage. 相似文献
6.
In the present study, forest type classification using Landsat TM False Colour Composite (FCC) bands 2, 3, 4 has been evaluated for mapping highly heterogeneous forest environment of Western Ghats (Kerala). Visual interpretation of Landsat TM FCC has been carried out to identify bioclimatic vegetation types. For accuracy estimation maps prepared from 1∶15,000 scale black-and-white aerial photographs have been used as ground check data. For comparison aerial photomap classes have been aggregated to match with Landsat-TM-derived map. The classification accuracy of ten major bioclimatic and landcover types was estimated using systematic sampling procedure. The overall classification accuracy of the forest types for the study area was 88.33%. 相似文献
7.
Three-date ERS-1 SAR data acquired on August 24, September 28 and November 2, 1995, was used to classify rice crop in a predominant rice growing region of West Bengal. India, Artificial neural network, maximum likelihood, decision rute and K-Means clustering classifiers were used. Classification accuracy was evaluated from the error matrix of same set of training and validating pixels. Rice classification accuracy improved significantly using neural network classifier. The decision rule based classifier performed equally good for most of the sites, indicating the feasibility of deriving a common rule based algorithm for large area application. Law aecuracy was observed for maximum likelihood classifier. 相似文献
8.
Indrani Choudhury M. Chakraborty S. C. Santra J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2006,34(1):23-37
Information on various agricultural resource parameters at various levels is essential for proper management and efficient
resource allocation for sustainable agricultural development. Limitations in ground-based method have encouraged the use of
satellite data coupled with geographical information system (GIS) in providing spatial as well as temporal information over
large and inaccessible areas. In the present study, an attempt has been made to generate raster maps using remote sensing
and GIS techniques to characterize the agroecosystem of South 24 Paraganas district of West Bengal, based on land utilization
indices. Information on multi-season landcover derived from the analysis of the multi-temporal RADARSAT-1 SAR and IRS-ID LISS
III data as well as other ancillary information in GIS environment are the basic inputs used in the study. The present analysis
shows that northern and northwestern parts are more diverse in terms of agricultural intensification as compared to the southern
and northeastern parts whereas the central parts show moderate density. In terms of carrying capacity, the high carrying capacity
has been observed in the southern to northeastern parts whereas the northwestern and central parts show moderate and northern
parts show low carrying capacity. Overall, the characterization of agroecosystem using land utilization indices can be identified
as major input to formulate a management plan for sustainable agriculture with concerns for the environment. 相似文献
9.
SAR数据在南方水稻分布图快速更新中的应用方法研究 总被引：9，自引：1，他引：8
研究表明,SAR数据对水稻识别和作物长势监测很有潜力。本文研究了在GIS的支持下,用SAR数据对南方水稻分布图进行快速更新的方法。首先,对多时相的SAR数据进行GammapMap滤波、几何精纠正和辐射干扰校正等处理;然后,采用最大似然法进行有监分类,获得水稻分布图;最后,对新、旧水稻分布图作标识码的Overlay处理,即可将水稻分布图更新。结果表明,该方法具有可操作性强,分类、制图精度高的特点。 相似文献
10.
Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines 总被引：1，自引：0，他引：1
This study developed an approach to map rice-cropping systems in An Giang and Dong Thap provinces, South Vietnam using multi-temporal Sentinel-1A (S1A) data. The data were processed through four steps: (1) data pre-processing, (2) constructing smooth time series VH backscatter data, (3) rice crop classification using random forests (RF) and support vector machines (SVM) and (4) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of rice-cropping patterns in the study region. The comparisons between the classification results and the ground reference data indicated that the overall accuracy and Kappa coefficient achieved from RF were 86.1% and 0.72, respectively, which were slightly more accurate than SVM (overall accuracy of 83.4% and Kappa coefficient of 0.67). These results were reaffirmed by the government’s rice area statistics with the relative error in area (REA) values of 0.2 and 2.2% for RF and SVM, respectively. 相似文献
11.
Stephen V. Stehman 《Journal of Geographical Systems》2006,8(2):209-226
Assessing thematic map accuracy is a special type of map comparison that is frequently applied to remote sensing classification problems. For map comparisons in the accuracy assessment setting, one map represents the classified output and the other map represents the true or “reference” condition. Several articles in this special issue describe state-of-the-art map comparison analysis tools that could serve to quantify accuracy of a single map. However, accuracy assessment objectives generally extend beyond describing accuracy of a single map to comparing accuracy of several maps. Consequently, interest focuses on comparing map comparison measures when these measures are used to represent accuracy. The virtual workshop emphasizes the analysis component of map comparisons, but it is also important to examine the underlying study designs generating the data input into these analyses. The study designs for accuracy comparisons implemented in remote sensing practice often investigate only a single test site, thus limiting our ability to generalize the results of these accuracy comparisons. Map accuracy comparison studies can be designed to provide stronger generalizations by incorporating experimental design principles such as replication and blocking, and identifying an experimental unit appropriate for the application. It is also important to recognize the role of statistical hypothesis testing and inference for different objectives that motivate map accuracy comparisons. Deciding which of two maps to use for a particular site can be addressed by enumerative inference and does not require hypothesis testing. For the objective of a more general comparison of classification procedures, analytic inference is appropriate and hypothesis testing plays a more prominent role. 相似文献
12.
N K Patel T T Medhavy C Patnaik A Hussain 《Journal of the Indian Society of Remote Sensing》1995,23(2):33-39
Microwave sensors having all-weather capabilities provide an opportunity to monitor rice grown in monsoon season. An attempt has been made to identify rice crop using multitemporal ERS-1 SAR data in C-band (5.3 GHz). Data acquired on August 15 (D1), September 19 (D2), October 24 (D3) and November 28 (D4) 1993 were taken. Combinations of data acquired on different dates were used for identification of rice crop. Single-date IRS-1B LISS II data in visible and NIR bands acquired on October 23, 1993 was also used for comparison of estimated rice area. Analysis of the results has shown that a combination of SAR data acquired at the tillering (August), booting (September) and heading (October) stages of rice crop enabled identification and area estimation of rice crop grown under lowland conditions. Single-date SAR data acquired in the month of October was found to be better for identification of rice compared to other dates. 相似文献
13.
A national level project on kharif rice identification and acreage estimation is being carried out successfully for several states in the country. A similar
methodology based on the temporal profile for identification and delineation of various land cover classes has been followed
for the Rabi rice acreage estimation. To define rabi rice, rabi season in India starts from November — February to March — June. Though the main growing season is predominantly winter but
the uncertainty of getting cloud free data during the season has resulted in the use of microwave data. A feasibility study
was taken up for early forecasting of the rabi rice area using microwave data. Hierarchical decision rule classification technique was used for the identification of the
different land cover classes. Land preparation, puddling and transplantation were the reasons for the specific backscatter
of rice growing areas. The increase or decrease in the SAR backscatter due to progress in the crop phenology or due to delayed
sowing respectively forms the basis for identifying the rice areas. In addition the potential of optical data of a later date
has been utilized in the form of various indices from bands including MIR to distinctly separate the late sown areas and also
the puddled areas from other areas. This study emphasizes the synergistic use of SAR and optical data for delineating the
rabi rice areas which is of immense use in giving an early forecast. 相似文献
14.
ALOS PALSAR双极化数据水稻制图 总被引：1，自引：0，他引：1
以江苏省海安县为研究区,使用2008年获取的日本ALOS卫星PALSAR双极化模式数据,分析水稻在L波段SAR图像上的后向散射特征,并提出相应的水稻制图方法。水稻在L波段上表现出了和C波段相同的时相变化特征。HH极化后向散射依赖于水稻植株的空间分布结构,某些机械插秧区域的布拉格共振现象引起水稻后向散射严重增强,给利用PALSAR数据水稻制图带来了困难。而HV极化不存在布拉格共振现象。在考虑布拉格共振影响的条件下,提出了联合PALSAR双极化模式HH和HV极化数据、基于时相变化特征进行水稻制图的方法,获得了88.4%的制图精度。 相似文献
15.
The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the Cloude–Pottier and Freeman–Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95% and Kappa of 0.93; a 6% improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude–Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman–Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps we were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phenological growth stage based on the BBCH scale, and the HV backscatter and entropy. 相似文献
16.
Single, interferometric dual, and quad-polarization mode data were evaluated for the characterization and classification of seven land use classes in an area with shifting cultivation practices located in the Eastern Amazon (Brazil). The Advanced Land-Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data were acquired during a six month interval. A clear-sky Landsat-5/TM image acquired at the same period was used as additional ground reference and as ancillary input data in the classification scheme. We evaluated backscattering intensity, polarimetric features, interferometric coherence and texture parameters for classification purposes using support vector machines (SVM) and feature selection. Results showed that the forest classes were characterized by low temporal backscattering intensity variability, low coherence and high entropy. Quad polarization mode performed better than dual and single polarizations but overall accuracies remain low and were affected by precipitation events on the date and prior SAR date acquisition. Misclassifications were reduced by integrating Landsat data and an overall accuracy of 85% was attained. The integration of Landsat to both quad and dual polarization modes showed similarity at the 5% significance level. SVM was not affected by SAR dimensionality and feature selection technique reveals that co-polarized channels as well as SAR derived parameters such as Alpha-Entropy decomposition were important ranked features after Landsat’ near-infrared and green bands. We show that in absence of Landsat data, polarimetric features extracted from quad-polarization L-band increase classification accuracies when compared to single and dual polarization alone. We argue that the joint analysis of SAR and their derived parameters with optical data performs even better and thus encourage the further development of joint techniques under the Reducing Emissions from Deforestation and Degradation (REDD) mechanism. 相似文献
17.
GNSS data management and processing with the GPSTk 总被引：2，自引：0，他引：2
Dagoberto Salazar Manuel Hernandez-Pajares Jose M. Juan Jaume Sanz 《GPS Solutions》2010,14(3):293-299
We organize complex problems in simple ways using a GNSS data management strategy based on “GNSS Data Structures” (GDS), coupled
with the open source “GPS Toolkit” (GPSTk) suite. The code resulting from using the GDS and their associated “processing paradigm”
is remarkably compact and easy to follow, yielding better code maintainability. Furthermore, the data abstraction allows flexible
handling of concepts beyond mere data encapsulation, including programmable general solvers. An existing GPSTk class can be
modified to achieve the goal. We briefly describe the “GDS paradigm” and show how the different GNSS data processing “objects”
may be combined in a flexible way to develop data processing strategies such as Precise Point Positioning (PPP) and network-based
PPP that computes satellite clock offsets on-the-fly. 相似文献
18.
《ISPRS Journal of Photogrammetry and Remote Sensing》1999,54(5-6):305-316
This paper describes the development of a 1-km landcover dataset of China by using monthly NDVI data spanning April 1992 through March 1993. The method used combined unsupervised and supervised classification of NDVI data from AVHRR. It is composed of five steps: (a) unsupervised clustering of monthly AVHRR NDVI maximum value composites is performed using the ISOCLASS algorithm; (b) preliminary identification is carried out with the addition of digital elevation models, eco-region data and a collection of other landcover/vegetation reference data to identify the clusters with single landcover classes; (c) re-clustering is performed of clusters with size greater than a given threshold value and containing two or more disparate landcover classes; (d) cluster combining is performed to combine all clusters with a single landcover class in one cluster, and all other clusters into one mixed cluster; and (e) supervised classification is used to carry out post-classification of the mixed cluster generated in the previous step by using the maximum likelihood algorithm and the identified single landcover classes of the previous step as training data. The classification is based on extensive use of computer-assisted image processing and tools, as well as the skills of the human interpreter to take the final decisions regarding the relationship between spectral classes defined using unsupervised methods and landscape characteristics that are used to define landcover classes. 相似文献
19.
Kamlesh P. Lulla Michael R. Helfert David L. Amsbury Victor S. Whitehead Cynthia A. Evans M. Justin Wilkinson 《国际地球制图》2013,28(1):69-80
Abstract Multi‐temporal ERS‐1 SAR data acquired over a large agricultural region in West Bengal was used to classify kharif crops like rice, jute and sugarcane. Rice crop grown under lowland management practice showed a temporal characteristic. The dynamic range of backscatter was highest for this crop in temporal SAR data. This was used to classify rice using temporal SAR data. Such temporal character was not observed for the other study crops, which may be due to the difference in cultivation practice and crop calendar. Significant increase in backscatter from the ploughed fields was used to derive information on onset and duration of land preparations. Synergistic use of optical remote sensing data and SAR data increased the separability of rice crop from homesteads and permanent vegetation classes. 相似文献
20.
Atmospheric delays are contributors to the GNSS error budget in precise GNSS positioning that can reduce positioning accuracy
considerably if not compensated appropriately. Both ionospheric and tropospheric delay corrections can be determined with
help of reference stations in active GNSS networks. One approach to interpolate these error terms to the user’s location that
is employed in Germany’s SAPOS network is the determination of area correction parameters (ACP, German: “Fl?chenkorrekturparameter—FKP”).
A 2D interpolation scheme using data from at least 3 reference stations surrounding the rover is employed. A modification
of this method was developed which only makes use of as few as 2 reference stations and provides 1D linear correction parameters
along a “corridor” in which the user’s rover is moving. We present the results of a feasibility study portraying results from
use of corridor correction parameters for precise RTK-like positioning. The differences to the reference coordinates (3D)
attained in average for 1 h of data employing selected network nodes in Germany are between 0.8 and 2.0 cm, which compares
well with the traditional area correction method that yields an error of 0.7 up to 1.1 cm. 相似文献