IMPROVED CLASSIFICATION OF CONSERVATION TILLAGE PRACTICES USINGHYPERSPECTRAL IMAGERY WITH SPATIAL-SPECTRAL FEATURES

Wei Li 1, Qiong Ran 1, Qian Du 2, Chenghai Yang 3

1 Beijing University of Chemical Technology, Beijing, China2 Mississippi State University, Mississippi, USA

3 USDA Southern Plains Agricultural Research Center at College Station, Texas, USA

ABSTRACT

Classification of conservation tillage practices from hyper-spectral imagery is challenging due to spectral similarity be-tween soils and senescent crop residues. In this paper, a novelclassifier using both spectral and spatial information is pro-posed for hyperspectral image classification. Three steps areincluded: (1) a feature extraction method using a very simplelocal averaging filter to produce the joint spectral-spatial fea-tures; (2) an efficient local Fisher discriminant analysis pro-jection for dimensionality reduction and class separability en-hancement; and (3) the typical k-nearest neighbor classifierfor final classification. Experimental results using real hy-perspectral data demonstrate the benefits of the proposed ap-proach, which can outperform other popular classifiers, suchas support vector machine with composite kernel.

Index Terms— Conservation tillage, hyperspectral data,feature extraction, pattern classification.

1. INTRODUCTION

Crop residues on the soil surface decrease soil erosion andrunoff rates, improve soil and water quality, and increasesoil organic matter, which are critical for sustainable cropproduction and environment [1, 2]. Thus, management ofcrop residues is an integral part of many conservation tillagesystems. Conservation tillage management has been advo-cated, which include those with minimal tillage (MT) andthose without tillage or no-till (NT). NT systems allow only15%∼25% surface disturbance and residue removal, whileMT systems do not disturb more than ∼ 67% of the surfaceresidue. Current methods to map crop tillage practices consistof drive-by, or commonly referred to as windshield surveys,to sample fields. These methods are costly, time-consuming,and cannot provide accurate spatial distributions of individualfields. On the contrary, rapid, accurate, and objective map-ping can be generated by using remote sensing technology.

However, classification of conservation tillage practicesfrom remote sensing imagery is challenging due to spectral

This work is supported by the National Natural Science Foundation ofChina under Grant No. NSFC-61302164.

similarity between soils and senescent crop residues [3, 4]. Inthis research, we will use hyperspectral imagery covering vis-ible to shortwave infrared spectrum to improve such classifi-cation. In particular, it is expected that the discrimination be-tween soil and senescent crop residues can be improved withthe shortwave infrared bands.

Taking advantage of the rich spectral information, numer-ous classification algorithms using hyperspectral data havebeen developed for a variety of applications [5]. With the de-velopment of sensing technology, hyperspectral images withhigh spatial resolution are continually becoming more avail-able; however, most of classifiers are still based solely onspectral signatures [6], ignoring spatial information at neigh-boring locations. Recent research efforts have paid more at-tention to the spatial information as well as the joint spatial-spectral features for hyperspectral image classification, pro-viding excellent performance [7].

In this paper, we will further investigate the use of spa-tial feature for the classification of conservation tillage prac-tices (i.e., MT and NT) from hyperspectral imagery. Specif-ically, a spatial feature extraction method using a very sim-ple local averaging filter (LAF) is proposed. The method po-tentially smoothes out trivial variations as well as noise ofhyperspectral data, and simultaneously exploits the fact thatneighboring pixels tend to belong to the same class with highprobability. The spectral-spatial features further enhanced bylocality-preserving-based discriminant analysis, followed bythe k-nearest neighbor (k-NN) classifier. We validate the pro-posed algorithm using real hyperspectral data to demonstratethe benefits and compare with existing methods.

2. PROPOSED METHOD2.1. Local Averaging Filter

Remote sensing images usually include many spatially homo-geneous areas. In this work, a block-based (local) averagingfilter is employed to extract spectral-spatial features. Specifi-cally, we perform spatial convolution with a sliding window,wherein the window of size B × B pixels is scanned acrossthe 3-D hyperspectral imagery (the size is M×N×D, whereM×N means the spatial size and D represents the number of

Fig. 1. Block-based averaging filter for hyperspectral im-agery.

spectral bands) as shown in Fig. 1. Such a vector-based spa-tial convolution is associated with a kernel. For simple localaveraging, all the values in the kernel are the same with thesum equal to one. The impact of block size is data dependent;in general, a larger block size can be adopted for an imagewith larger homogeneous areas.

The LAF proposed in this work is a simple and easymethod to implement, which can potentially reduce triv-ial spectral variation and noise of hyperspectral data. Themethod extracts spectral-spatial features from neighboringpixels. The essence is to replace the central pixel value withthe mean value of its surroundings, including itself. It encour-ages spatial homogeneity of the classification and smoothesout boundary-like artifacts.

2.2. Local Fisher Discriminant Analysis

Local Fisher discriminant analysis (LFDA) [5] is a super-vised dimensionality-reduction technique which is designedto handle multimodal, non-Gaussian distributions. In essence,LFDA combines the properties of linear discriminant analysisLDA and local preserving projection (LPP) [8]. In LFDA, thelocal between-class S(lb) and within-class S(lw) scatter matri-ces are defined as,

S(lb) =1

2

n∑

i,j=1

W(lb)i,j (xi − xj)(xi − xj)

⊤, (1)

S(lw) =1

2

n∑

i,j=1

W(lw)i,j (xi − xj)(xi − xj)

⊤, (2)

where W (lb) and W (lw) are n× n matrices defined as,

W(lb)i,j =

{

Ai,j(1/n− 1/nl), if yi = yj = l,

1/n, if yi 6= yj ,(3)

W(lw)i,j =

{

Ai,j/nl, if yi = yj = l,

0, if yi 6= yj.(4)

Fig. 2. Pesudo-color image of Indian Pines dataset.

Maximizing the Fisher’s ratio as defined using the local scat-ter matrices, we have that

ΦLFDA =

argmaxΦLFDA

tr

[

(

Φ⊤

LFDAS(lw)ΦLFDA

)−1

Φ⊤

LFDAS(lb)ΦLFDA

]

(5)

is given by S(lb)ΦLFDA = ΛS(lw)ΦLFDA, where Λ is the diago-nal eigenvalue matrix, and ΦLFDA ∈ R

d is the transformationmatrix. LFDA achieves better between-class separation in theprojection while preserving the within-class local structure atthe same time.

2.3. K-Nearest Neighbor

In above subsection, LAF is employed to extract spectral-spatial features and LFDA is used to reduced the dimensional-ity and class separability. After these, the typical k-NN clas-sifier is considered for the final classification. The nearestneighbor (NN) classifier attempts to find the training samplenearest to the testing sample according to a given distancemeasure, and assigns the former’s class label to the latter.Commonly, Euclidean distance is used to measure the sim-ilarity between a training sample xi and a testing sample y,

d(xi,y) =∥

∥xi − y∥

∥

2

2. (6)

The k-NN classifier [9] is a straightforward extension of theoriginal NN classifier. Instead of using only one sample clos-est to the testing point y, the k-NN classifier chooses the knearest samples from training data X, and majority voting isemployed to decide the class label of y.

3. EXPERIMENTS

The experimental hyperspectral dataset employed was ac-quired using National Aeronautics and Space AdministrationsAirborne (NASA) Visible/Infrared Imaging Spectrometer(AVIRIS) sensor and was collected over northwest Indiana’sIndian Pine test site1 in June 1992. The image represents

1http://www.ehu.es/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes

10 15 20 25 30 35 40 45 5060

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Number of Training Samples per Class

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10 15 20 25 30 35 40 45 5065

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(a) Corn Sub-scene (b) Soybean Sub-scene

Fig. 3. Classification accuracy with standard deviation versus varying number of training samples per class for the proposedmethod using the experimental data.

a vegetation-dominated scenario with 145 × 145 pixels asshown in Fig. 2, and 220 bands in the 0.4- to 2.45-µm region.The spatial resolution is 20 m. The original scene includes16 classes and in our experiments, 4 related classes are in-volved: Corn-MT, Corn-NT, Soybean-MT, and Soybean-NT.The numbers of labeled samples for these 4 classes are 830,1428, 2455, and 972, respectively.

We investigate the proposed classifier for conservationtillage practices (i.e., MT and NT) classification. Thus, wesplit the original data into two sub-scene—Corn sub-scenewith ground truth map as shown in Fig. 4 (a) and Soybeansub-scene with ground truth map as shown in Fig. 5 (a).The proposed method is compared with the traditional sup-port vector machine (SVM) and SVM with composite kernel(SVM-CK) [10]. The parameters of all these algorithms aretuned via cross validation, and classification accuracy withoptimal parameters are provided.

Fig. 3 illustrate the classification accuracy versus varyingnumbers of training samples per class using the experimentaldata. The number of the testing samples are the rest fromthe labeled data. To avoid any bias, we randomly choosethe training and testing samples, repeat the experiments 20times, and report the average classification accuracy. Fromthe results in Fig. 3, the proposed classifier (with k = 3) out-performs other three classifiers, and even the state-of-the-artSVM-CK. For example, when the number of samples is 50for the Soybean sub-scene, the proposed classifier can obtainan accuracy of 96%, while the SVM-CK performs with 90%,resulting in an improvement of approximately 6%. Fig. 4 andFig. 5 further illustrate the classification maps resulting fromthe proposed approach as well as some traditional classifierusing the two experimental sub-scenes. It is obvious that themap of the proposed classifier is less noisy than others. The

corresponding classification maps further verify the obviousadvantages of the proposed approach.

4. CONCLUSIONS

We proposed a new classifier to investigate the use of spatialfeature for the classification of conservation tillage practices.The experimental results showed that the proposed spatial-spectral classification could provide even better results thanthe standard SVM, and SVM-CK using spatial-spectral fea-tures. They demonstrated that the proposed approach facili-tates superior discriminant features extraction, thereby yield-ing significant improvement in the classification of conserva-tion tillage practices.

5. REFERENCES

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[2] E. Choe, S. Hong, and Y. Kim, “Down-scaling of satel-lite hyperspectral images for monitoring croplands,” inProceedings of the International Geoscience and Re-mote Sensing Symposium, Honolulu, Hawaii, July 2010,pp. 3873–3874.

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(a) Ground truth

(b) k-NN: 72.05% (c) SVM: 82.73%

(d) SVM-CK: 94.69% (e) Proposed: 95.44%

Fig. 4. Classification maps of the proposed method as wellas the traditional SVM and SVM-CK using for the Corn sub-scene data with 50 training samples per class.

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[7] W. Li, S. Prasad, Z. Ye, J. E. Fowler, and M. Cui,“Locality-preserving discriminant analysis for hyper-spectral image classification using local spatial informa-tion,” in Proceedings of the International Geoscienceand Remote Sensing Symposium, Munich, Germany,July 2012, pp. 4134–4137.

[8] X. He and P. Niyogi, “Locality preserving projections,”

(a) Ground truth

(b) k-NN: 79.08% (c) SVM: 81.15%

(d) SVM-CK: 89.96% (e) Proposed: 97.11%

Fig. 5. Classification maps of the proposed method as wellas the traditional SVM and SVM-CK using for the Soybeansub-scene data with 50 training samples per class.

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