[ieee 2012 first international conference on agro-geoinformatics - shanghai, china...

Recognition of Corn Acreage in Jilin Province Based on Mixed Pixels Decomposition

LIU Jia 1. Key Laboratory of Agri-informatics

Ministry of Agriculture 2. Institute of Agricultural Resources and Regional Planning

Chinese Academy of Agricultural Sciences Beijing, China

E-mail: [email protected]

HUANG Yan Esri China Information Technology Co., Ltd

Beijing, China E-mail: [email protected]

LI Dandan 1. Key Laboratory of Agri-informatics




WANG Limin 1. Key Laboratory of Agri-informatics




YANG Fugang 1. Key Laboratory of Agri-informatics




WANG Xiaolong 1. Key Laboratory of Agri-informatics




Abstract—To improve the application ability of the low resolution remote sensing image in crop area remote sensing monitoring, the corn of Jilin province is selected as the object of study. Based on the remote sensing zoning of corn planting area in the research area, and based on middle and low resolution remote sensing data, the corn area mixed-pixel decomposition model based on neural network function is constructed by using Levenberg-Marquard network optimization algorithms and by taking the smallest root-mean-square error as the condition of iteration termination. The input of the model is the EOS-MODIS NDVI time series data of 18 ten-days during the corn growing season from April to September of 2011, with the spatial resolution of 250m; the output is the corn area value of corresponding resolution. The endmember of corn area comes from the maximum likelihood classification result of the SPOT4 image, with the spatial resolution of 20m. Comparing the decomposition results of mixed pixels from the 3 counties of Dehui district with the the results of the background investigation made in 2011, the precision reached 86.1%. The paper attempts to use high resolution images to directly extract crop endmember. Compared with the endmember extraction methods such as Maximum Noise Fraction (MNF), Pixel Purity Index, Principal Component Analysis, and convex cone geometry theory, etc, it has more intuitive effect, more definite physical

significance, stronger operability, and larger business operation potential.

Keywords- mixed pixels; corn acreage; remote sensing recognition; neural network

I. INTRODUCTION Due to the longer repetition period and easier data

acquisition, EOS-MODIS, SPOT-VGT and other satellite data with low resolution from 250m to 1000m still serve as the main source of data for rapid recognition of regional crop areas[1]. Component units (or endmembers) are various land cover types with a single spectrum that make up of mixed pixels. The common existence of mixed pixels makes it difficult for the pixel-level area measurement accuracy to meet the application requirements. The mixed pixels decomposition technology unmixes pixels into a variety of basic component units (or endmembers) and improves the measurement accuracy. It has become the major technical approach to take full advantage of low resolution satellite data, among which endmember selection and the abundance decomposition method are the two critical steps in mixed pixels decomposition.

This work was funded by international technology cooperation project of Ministry of science and technology (2010DFB10030), the other project of Ministry of Agriculture (2011-G6).

In general, there are three schemes about endmember selection: the first is to extract reference units according to field spectrum measurement or from the existent geography objects spctrum information base; the second is to select image endmembers directly from the unspecified images; the third is to make endmember selection by means of reference endmember correction or image endmember adjustment. Scatter diagrams (PCA or MNF), pixel purity indexes (PPI) and convex cones geometry theory model (N-Finder) are commonly-used methods in image endmember selection[2]. Major modes of abundance decomposition mainly include the linear mixing model, fuzzy classification model and blind source separation. The first is the simplest in theory and most widely used; besides, there are such intelligence artificial algorithm as neural network classification, which, to a certain extent, resembles the complicated process of mixed pixels formation and enjoys higher accuracy in its decomposition results than in a simple linear model[3].

The above endmember selection technology is used at provincial or national levels, which determines the endmember components based on experts' experience and in which various endmember characteristics take up relatively great changes in different geographic regions. Therefore, great difficulties exist in its practical applications. In view of the fact that multi-spectral remote sensing images of medium or high spatial resolutions from 10 to 30m have been widely used in agricultural remote sensing[4], we took corn areas in Jilin province as the research object, the SPOT4 images directly as the endmember recognition approach, and the EOS-MODIS as the object to be decomposed and made corn acreage extraction in the research area with neural network classification method, getting the spatial distribution of corn areas within Jilin province in 2011, whose accuracy could meet the requirement of regional business monitoring.

II. STUDY AREA AND DATA Jilin province lies in the northeast of China, covers the area

of 121°-131°E, 41°-46°N, and takes up 187,000 km2. It is in temperate continental monsoon climate and has a frost-free period of 100 to 160 days, annual mean temperature of 2-6�, and mean annual precipitation of 400-600mm which varies with the change of seasons and regions and 80% of which concentrates in summer, especially in the east part of the province. The west part of the province is the Songliao Plain, an area of temperate semi-humid climate and temperate semi- arid climate mainly covered by alluvial and sandy plain and alluvial and pluvial plain; the east part is the Changbai mountain region of temperate humid climate, where medium hills, low hills and valley plains ranging from northeast to southwest scatter except for the volcanic Changbai Mountain. Jilin province is abundant in corn, rice, soybean, grain sorghum, millet, wheat, potato, grain, etc. According to statistics 2010, its crop planting area reaches 5.2 million hectares, of which corn accounts for 3.4 million hectares, 67.8% of the total food crop planting area; rice 15.0%, soybean 8.4%, and other crops 8.8%.

The data adopted in this paper consist of basic data and remote sensing data. The former include soil data of Jilin province, DEM, agricultural geomorphic regionalization data

of Jilin province and 2010 corn planting areas of counties of Jilin province. The latter include ten-day maximum synthesis of EOS/MODIS NDVI during April 1 and September 30 of 2010 (18 ten-days in total); MODIS data have been through atmospheric correction and NDVI of 250m resolution and in time sequence have been through S-G filtering processing [5, 6]; high-resolution remote sensing data are SPOT4 20m multi-spectral images.

III. METHOD The process of mixed pixels decomposition is illustrated on

figure 1. Including procedures like basic data processing, remote sensing corn areas division, SPOT4 sample areas selection, functional construction of neural networks in sample areas, regional mixed pixels decomposition and accuracy verification. Detailed contents of the key procedures are as follows.

Figure 1.Technology process of mixed pixels decomposition

A. Remote sensing corn areas division in Jilin province Define the ratio of the corn planting area of each county to

that of the whole province as the basic classification value, and get the division units based on areas using cluster analysis technique. Analyze the relationship between climatic and geomorphologic factors of corn planting areas and per unit area yield to identify major environmental factors influencing per unit area yield of corn; rank the major environmental factors of each county to make the optimal second division and determine the division units based on per unit area yield; make the optimal second division for more than twice if needed in the research. Integrate the division units based on areas and per unit area yield with expert knowledge and establish the preliminary remote sensing division areas of corn covering the whole province; considering the influence of landform on remote sensing spectral features, adopt landform division boundaries to correct the preliminary remote sensing areas and get the final remote sensing corn areas division in Jilin province.

Yes

S-G filtered NDVI

EOS-MODIS NDVI (10-day composite )

Corn acreage Division

SPOT4 Images

Endmember Selection

BP Neural Networks Model

Mixed Pixels Decomposition

Corn Acreage Estimation

Accuracy Verification No

B. SPOT4 sample area selection and corn acreage recognition Based on the remote sensing corn areas division in Jilin

province, corn cultivation conditions in different zones and the spectral response characteristics of the crops in EOS/MODIS data are taken as comparatively similar and a shot of SPOT4 data are selected from each of the four typical corn planting areas under remote sensing to be the endmember data source needed in mixed pixels decomposition. Extract four shots of SPOT4 corn acreage by means of maximum likelihood classification and visual correction.

C. BP neural networks based on Levenberg-Marquardt algorithm The feedforward neural network BP model is the most

commonly used neural network model. The gradient descent algorithm is the standard algorithm of the BP model, but the problem of slow convergence speed of the local minimum and learning algorithm stands out especially when it is used in immense amount of calculation of image endmembers. This paper establishes a three-layered BP neural network model with Levenberg-Marquardt algorithm, which is in nature a kind of Quasi-Newton algorithm. It does not need calculation of Hessian matrix and can thus greatly improve the practice speed of the network. For detailed procedures, please see reference [7]. The construction of a three-layered BP neural network model are as follows.

Given the numbers of nodes of the input layer, hidden layer and output layer are respectively n, p and q. Nodes in adjacent layers are fully connected through connection weight while nodes in the same layer are not connected at all. The output model of the hidden layer is :

j

n

iiijj xwfh θ+= ∑

=

)(1

(1)

The output model of the output layer is :

k

p

jjjk hwfy θ+= ∑

=)(

1k (2)

Where, Wij(i=1,n;j=1,p) is the weight of the input layer to the hidden layer, Wjk(j=1,p;k=1,q) is the weight of the hidden layer to the output layer, θj is the threshold value of the neurons in the hidden layer, θk, is the threshod value in the output layer.Once the network structure is defined, the weights Wij and Wjk and the threshold values ofθ j andθ k can be determined through network learning and the model is established.

D. Accuracy verification It includes model verification about SPOT4 sample area

data and reginal mixed pixels decomposition accuracy after the extension of the model. The former is performed by reserving 15% of the training samples and the latter is done using background Investigation data of each county. The minimum RMS error method or relative error method is adopted for verification.

IV. RESULTS Using the above methods, the research reaches the

following conclusion in remote sensing corn areas division in Jilin province, SPOT4 sample area selection and corn acreage recognition, mixed pixels decomposition based on neural network algorithm, sample units and regional accuracy verification, and spatial distribution and number of corn areas in Jilin province.

A. Remote sensing corn areas division in Jilin province Divide Jilin province into 8 clustering units according to

the corn areas of each county; based on references, the precipitation under temperature not lower than 10� and the accumulated temperature under spring temperature of 8� and autumn temperature of 10� are the two influencing factors on the per unit area yield of corn in Jilin province. Make the optimal division for twice based on the two factors to get the division areas in terms of per unit yield affected by meteorological factors; integrate the area divisions and the per unit yield divisions and take advantage of the agricultural geomorphic regionalization of Jilin province to make corrections of regional boundaries. And then the province of Jilin is divided into four regions: western alluvial and sandy plain corn area, central alluvial and pluvial plain corn area, central hilly corn area and eastern low mountains corn area. In terms of the maturity date, the corn in the the four regions are generally late-maturing or mid-late maturing, medium maturity, mid-early maturity, early-maturing and extremely early maturing; in terms of yield, the four regions fall generally into categories of unstable low yield, stable low yield, stable high yield, stable medium yield and unstable yield.

The division boundaries do not perfectly overlap with county boundaries. When a county is located in two corn division areas, split the acreage data with the land area as the weight to ensure the precision of statistical data of corn acreage in each division area. According to the statistics in 2010, the corn planting areas in the four regions take up 18.5%, 58.5%, 13.2% and 9.8% of the total area of the whole province in turn.

B. SPOT4 sample area selection and corn acreage recognition Select SPOT4 images with the highest crop proportion

from images covering the geometric centers of the four corn division areas. This paper adopts 4 scenes of SPOT images on August 12, August 17, September 6 and September 10 from east to west, whose track numbers are respectively 305/263, 299/263, 297/261 and 292/259. Classification of SPOT corn images is exercised by maximum likelihood classification and the spatial resolution of the result is 20m. Take the corn sample plot data of ground survey as the truth conditions and verify with the confusion matrix. The overall accuracy turns out to be over 95% and the average Kappa index is 0.84. In the four regions, the corn areas covered by SPOT4 are respectively 15.0%, 8.5%, 22.39% and 15.2% of the total corn area, indicating the typicalness of the SPOT4 sample area selection. Figure 2 shows the corn planting areas division and the distribution of SPOT4 sample areas of Jilin province.

Figure 2.The corn planting areas division and the distribution of SPOT4 images

C. Mixed pixels decomposition based on neural network algorithm Abundance acquisition of endmembers with 250m spatial

resolution based on SPOT4 corn area classification. Take the 20m classification results of SPOT4 as the training samples and get the 250m MODIS/NDVI time sequence images and 20m classification results; spatially align the 250m NDVI time sequence images and 20m classification results to make sure every 250m endmember corresponds to 25×25 10m classification results endmembers; define the abundance of corn endmembers based on the 25×25 10m classification results endmember corresponding to each 250m pixel; finally, get the 250m corn abundance image which enjoys a one-to-one correspondence with each pixel of MODIS/NDVI image in the training sample area.

Establishment of BP neural network model between corn endmember abundance and NDVI time sequence images in the training sample area. It includes an input layer, a hidden layer and an output layer. There are 18 input neurons, which means the ten-day NDVI images within the growth period of corn in Jilin province; there are 1 output neuron, i.e. the corn area component; through repeated testing, the empirical value of 10 hidden neurons is adopted; the optimization algorithm of networking training is Levenberg-Marquardt algorithm. Before the network training, randomly select 70% samples as the training samples 15% as the cross validation samples, and 15% as the test samples to make cross validation and testing in network training. Make expert evaluation of results with over 90% of cross validation and test accuracy, and take the results as the final model results.

Extrapolate the provincial corn abundance with the neural network model of the four remote sensing areas of Jilin province. Input the NDVI time sequence images of each remote sensing division area of Jilin province into its corresponding neural network model and extrapolate the corn abundance image of the area and extract the corn/noncorn endmembers from the NDVI time sequence images; mosaick the corn abundance images of the four remote sensing areas into a panorama corn abundance image of the whole province and take mean values for overlapped areas; present the the distribution of corn areas in Jilin province as is shown in Figure 3 through graded tint.

Figure 3.Spatial distribution of Jilin corn areas

D. Sample units and regional accuracy verification Considering the sample accuracy and regional accuracy, in

each sample area, there are 32,000 to 55,000 training samples, of which 70% are testing samples and 15% are cross validation samples. The relative errors of the cross validation samples from the four areas are respectively 0.63, 0.55, 0.60 and 0.58 from east to west, all proving statistical correlation and proving the high accuracy of neural network models established in the sample areas.

Investigations of corn planting areas in the three counties of Dehui, Jiutai and Nong'an subordinate to Dehui City are made by visual interpretation of SPOT4 and SPOT5 images in 2011. The results are taken as the truth conditions of corn planting areas of the three counties, which are used to make verification of mixed pixels decomposition results (TABLE 1). The errors are between -17.7% and -12.1% with the mean value of -13.9%, indicating the regional accuracy of mixed pixels of over 80%.

TABLE I. ACCURACY VERIFICATION BASED ON BACKGROUND INVESTIGATION DATA

Name of county

Background area (Ha)

Decomposition area (Ha)

Error ratio (%)

Dehui 252.1 207.5 -17.7

Jiutai 167.7 146.0 -12.9

Nong'an 462.0 406.2 -12.1

Total 881.9 759.7 -13.9

E. Spatial distribution of corn acreage in Jilin province According to this research, the total corn area of Jilin

province in 2010 is 3324.6Ha, of which the western alluvial and sandy plain corn area, central alluvial and pluvial plain corn area, central hilly corn area and eastern low mountains corn area take up 595.1Ha, 1911.7Ha, 522.0Ha and 305.9Ha respectively, meaning 17.6%, 57.5%, 15.7% and 9.2% of the total. As to the composition of endmembers, the major corn planting area in Jilin is the central alluvial and pluvial plain corn area, where the percentage of pixels close to being pure is the highest, and the ratio of pixels whose area components are over 50% is 48.0%. This area is the flattest in Jilin province

and is also the region with the highest concentration of corn planting, so the corn purity presented with pixels of 250m spatial resolution reaches the top level. Following the sequence are the central hilly corn area and eastern low mountains corn area, where ratios of pixels with area components over 50% are 5.0% and 4.0% in turn. Corn planted in large areas of broad valleys and gentle slopes, corn yield in these two areas is still high and so is the pixel purity. Finally comes the western alluvial and sandy plain corn area, where the ratio of pixels whose area components are over 50% is 9.0%. As the concentration area of Khorchin sand land, this area is mostly occupied by grain planting and shows a low percentage of proximate pure pixels.

V. CONCLUSION A combined use of data with medium-high and medium-

low resolutions is a major tendency in the remote sensing application in current agriculture. With the rapid development of space technology, remote sensing images of medium or high spatial resolutions over 30m has been increasingly used, but their temporal resolutions are generally low. In large-scale crop area investigations with remote sensing technology, taking corn areas in Jilin province as an example, the optimal duration of remote sensing monitoring is from mid-July to the end of August, so it is difficult for the current remote sensing images with medium or high spatial resolutions to cover all areas of the whole province within the monitoring period. By contrast, EOS-MODIS and other images with lower spatial resolutions but higher temporal resolutions can repeat the coverage of the monitoring area in a short period. Therefore, in the remote sensing agricultural monitoring of many countries, the source data with medium-high and medium-low resolutions are used simultaneously to make crop acreage recognition.

The mixed pixels decomposition technology in this paper has potential business application abilities. With endmember information acquired from 20m SPOT4 multi-spectral images, and with neural network models, MODIS pixels of 250m spatial resolution can be decomposed, corn planting information in MODIS images can be reconstructed and the area accuracy can exceed 86.1%. The establishment of decomposition technical scheme of mixed corn pixels based on corn areas division and the selection of remote sensing images with medium-high resolutions is simple, clear and easy to operate, and the process has the potential of widespread use in monitoring of agricultural remote sensing areas[8]. As a matter of fact, the algorithm has been specified in the "operation system of remote sensing monitoring over agriculture" set up by the Ministry of Agriculture and has thus formed elementary professional ability.

The selection method of high-resolution image sample area needs further improvement. In this paper, the selection of SPOT4 sample areas are conducted based on remote sensing corn division areas and only takes the typicalness of the sample area into account, without consideration of the probability distribution of different endmembers in the area. Therefore, it is possible that the effects of some endmember groups with lower probability distribution are exaggerated, which will exert impact on the acreage recognition accuracy. A possible solution to this problem is to select the network training samples of neurons in line with the probability distribution of endmember groups in the research area or SPOT4 sample area.

ACKNOWLEDGMENT This paper is attributed to Professor Herman Eerens and

Dr. Qinghan Dong from VITO/TAP of Belgium as they offered much helped with the idea of this paper and the solidification of software.

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[ieee 2012 first international conference on agro-geoinformatics - shanghai, china...

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