Due to the high similarity of the spectra of urban water and building shadows, high-resolution satellite imagery often confuses and wrongly classifies these features. To address this problem, we propose an object-based method for distinguishing building shadow from water using an artificial bee colony algorithm. In the method, four spectral ratio bands are first calculated as additional input parameters for improving the accuracy of segmentation results. During the segmentation, a series of statistical factors, such as spectrum, ratio, and sharp features, are calculated to make up for defects in the high-resolution imagery. Finally, we propose a fuzzy-rule-based classifier to generate extraction rules. The classifier is based on artificial bee colony optimization, which employs the geometric mean (
Mapping of remote sensing imagery is widely used for surveying urban land resources, monitoring natural disasters, land-use planning, and so on. With the rapid development of technology concerning high-resolution satellite imagery (HRSI) such as IKONOS and QuickBird, it is possible to identify small-scale features such as roads and buildings in urban environments. HRSI has been shown to be an effective means for generating urban digital images with positional accuracies. High-resolution monitoring of urban environments remains a challenge, however, because of the complex nature and diverse composition of features within such areas. Many features found in the urban environment are spectrally similar (such as shadow and water), leading to problems in automated and semiautomated classification methods for urban areas. The principal problem caused by the shadow effect is either a reduction or total loss of information; this is particularly significant in high-density urban areas where shadows are cast by buildings. Moreover, building shadow is a typical source of noise during the extraction of water or other dark objects because such objects are hardly separated by their spectra. Conventional methods, such as the normalized difference water index (NDVI), maximum likelihood, and minimum distance from mean, have difficulty distinguishing water from building shadow in high-resolution imagery because they utilize only spectral information. Spatial information, such as texture and context, should be exploited to improve the accuracy of classifying spectrally similar objects.
Many algorithms have been proposed for identifying water bodies by HRSI. These methods can be divided into four categories: single-band threshold, multiband ratio, supervised or unsupervised classification, and linear unmixing [
To solve the problem, researchers have introduced an abundance of shadow detection methods into water indices [
The image classification method is another option to extract features, including water and building shadow. Supervised and unsupervised classification can be accomplished using either pixel-based or object-based approaches. In addition to spectral and textural information utilized in pixel-based classification methods, object-based methods also use geometrical characteristics and topographic relationships for classification [
Another advantage of fuzzy classification methods is that they have the most intuitive representation. The simple rules of fuzzy classifiers, which realize the formulation from the feature space to the class space through a set of IF-THEN rules, can employ the enhanced search capabilities of swarm algorithms such as particle swarm optimization (PSO), ant colony optimization, and artificial immune system.
Recently, a new intelligence theory, artificial bee colony (ABC), has also been applied for solving optimization problems. ABC is an emerging technology in swarm intelligence algorithms; it was proposed by Karaboga in 2005 [
To address these problems, we propose a method for distinguishing building shadow from water in high-resolution satellite images. The method consists of two steps. First, we employed an image segmentation algorithm to obtain homogeneity objects from a high-resolution image. As the four available bands may not address the whole problem, we involved four bands of ratio to calculate each object’s geometrical and topographic characteristics. Second, we provided a fuzzy-rule-based classifier to distinguish shadow and water segments. The classifier applied a customized ABC algorithm for optimizing the best fitness function, that is, the classification accuracy of water and shadow.
The study area was Xiamen City (Figure
The study area and two test sites of the GF-1 image composed with bands 4, 3, and 2.
The GaoFeng-1 (GF-1) image was acquired in May 2014. The GF-1 satellite is the first satellite in China’s high-resolution earth observation system and was launched on April 26, 2013. Its WFV sensors can obtain 16 m resolution multispectral color and 2 m resolution panchromatic images. The experimental image was treated by image preprocessing, including orthorectification, geometric correction, atmospheric correction (FLAASH), and image fusion. The original data included four bands: blue (0.45–0.52
Because our only focus was distinguishing between water and building shadow, thresholding based on the images’ brightness was used to mask most other land-cover classes before image segmentation; the rest of the objects were collected as samples through manual selection. The reference data used for accuracy assessment was acquired mainly by visual interpretation. Approximately 350 randomly distributed polygons were manually digitized at both sites. Dataset A had 1213/58 training pixels/objects and 11614/98 pixels/objects as reference, and dataset B had 4237/58 training pixels/objects and 20361/126 pixels/objects as reference.
Many studies have extracted shadows from high-resolution images using pixel-based methods. Such methods, however, produce a number of errors. To reduce those errors, in this study we employed an object-based method in which objects are identified by number of pixels, shape, and other criteria. The fuzzy-rules extraction method was based on ABC optimization, namely, F-ABC. A flowchart of the F-ABC method is shown in Figure
Flowchart of the F-ABC method.
There are several object-oriented image segmentation algorithms, but most algorithms cannot integrate spectral and special information and differ in efficiency and results. Due to the lack of a standard evaluation system, an optimal segmentation scale does not exist, so the optimal scale must integrate with specific remote sensing images manually. The segmentation algorithm used in this study is similar to the multiscale segmentation algorithm in eCognition software [
Restricted by the quality of sensors, the original high-resolution imagery has information based on only four bands, which is insufficient for producing high-quality results. To improve segmentation and subsequent classification results, four additional ratio bands, including blue/green, green/red, red/NIR, and NDVI, were calculated through ArcGIS’s band math tool. Concerning scale selection, the initial scale was set at 70 and the terminate scale was set at 250. Finally, with eight bands as input features, the segmentation maps were generated through the multiscale image segmentation method. The number of objects at different scales is shown in Table
The statistic result of multiscale segmentation of imagery.
Segmentation scale | 70 | 84 | 100 | 120 | 145 | 174 | 209 | 250 |
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Number of objects | 23618 | 15130 | 9062 | 5072 | 2598 | 1232 | 516 | 103 |
The segmentation results at different scales, composed with bands 4, 3, and 2. (a) Segmentation scale is 70. (b) Segmentation scale is 100. (c) and (d) are enlarged views of the squares in (a) and (b).
Figure
Subsequently, we collected statistical information on each object in the segmentation map at scale 70. That information was divided into three categories: spectrum, ratio of multibands, and geometrical features. Among them, the spectral features included mean value, maximum value, and minimum value on each band. Ratio of multiband features included spectral brightness, standard deviation, and ratio of maximum and minimum brightness values. Object shape features included area, convexity, hardness, generate rate, decurrent rate, shoxen rate, shape index, and length-width ratio. Due to the existing correlation effect among features, however, including all the features in the classification would increase the complexity of computation and might therefore affect accuracy. Thus, all objects as training samples were subjected to Pearson’s correlation analysis. Features with correlation coefficient values larger than 0.7 were excluded. Some 13 features were finally selected, including number of pixels, average DN value of spectral bands and ratio bands, standard deviation, and shapes. Description of the statistical information is shown in Table
Features of segmentation objects.
Category | Feature name | Description | Band number |
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Spectrum | B1–B4 | Average DN value of band | 1–4 |
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Multiband ratio | B5–B8 | Average DN value of ratio band | 5–8 |
Brt | Object brightness | 9 | |
Std | Object standard deviation | 10 | |
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Geometrical features | NUMPIX | Number of pixels | 11 |
ROUND | Shoxen rate | 12 | |
LWR | Length-width ratio | 13 |
A standard ABC model includes four main parts: (1) extraction rule definition, (2) fitness function construction, (3) global/local neighborhood search, and (4) overlapping objects prediction. To apply ABC to the extraction of water and building shadow, the original ABC algorithm had to be reconstructed. Specific content was performed as follows.
The confusion matrix of binary classification.
Class | Water (predicted) | Building shadow (predicted) |
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Water (actual) | |
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Building shadow (actual) | |
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To validate the extraction result, a widely used pixel-based SVM classifier was also adopted in this experiment. Error matrices and kappa coefficient were both used for accuracy assessment. The comparison between SVM and our proposed method was based on the same datasets derived from two test sites. The only difference was that the SVM method is pixel-based and our proposed method is object-based. The algorithm parameters of ABC were set as bee colony scale = 200, limit search number = 200, and maximum cycle number = 600.
Figure
Comparison of water and building shadow extraction results using F-ABC and SVM on two test images. Enlarged views of the highlighted regions are shown in Figure
Enlarged view of the highlighted regions using F-ABC and SVM at the two test sites.
Based on reference datasets of the two test sites, the statistical comparisons are shown in Tables
(a) Comparison of confusion matrixes using F-ABC and SVM for site A. (b) Comparison of confusion matrixes using F-ABC and SVM for site B.
Class | SVM | F-ABC | ||||||
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Water | Shadow | Others | PA% |
Water | Shadow | Others | PA% | |
Water | 4975 | 1390 | 28 | 77.82 | 5332 | 794 | 267 | 83.40 |
Shadow | 130 | 4545 | 546 | 87.05 | 182 | 4852 | 187 | 92.93 |
UA% |
97.45 | 76.58 | 96.70 | 85.94 | ||||
OA% |
81.97 | 87.69 | ||||||
Kappa | 0.6588 | 0.7633 |
Class | SVM | F-ABC | ||||||
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Water | Shadow | Others | PA% |
Water | Shadow | Others | PA% | |
Water | 6494 | 259 | 3353 | 64.25 | 9353 | 340 | 413 | 92.55 |
Shadow | 0 | 8111 | 2144 | 79.09 | 78 | 8347 | 1830 | 81.39 |
UA% | 100 | 96.91 | 99.17 | 96.09 | ||||
OA% | 71.73 | 86.93 | ||||||
Kappa | 0.5591 | 0.7646 |
Compared to SVM results, the producer’s accuracy for each class was improved when using F-ABC. In particular, for water at site B, Table
In this study, we found that the improvement of accuracies using F-ABC could benefit from two aspects. First, with object-based image segmentation, pixels were generated as an object with its complete shape. Pixels of ambiguous spectrum could therefore be counted as an object and be involved in the extraction procedure. For example, more than 3,000 reference pixels were identified by F-ABC as either building shadow or water for site B, whereas they were misclassified as other by the SVM classifier. Second, these improvements further indicated that segmented objects might contain the information necessary for distinguishing water from building shadow.
The building shadow and water detection method in F-ABC was used to address the misidentification caused by overlapping spectral signatures of closely related features. Figure
The segmentation procedure also improved the extraction of water bodies. Figure
Spatial measures extracted from the segmentation procedure can decrease the rate of misclassification for the spectrally similar water/shadow classes. Other classes, however, should have their own best-suited spatial measures. Toward that end, we developed a fuzzy classification scheme to overcome the overlapping objects prediction problem. The ABC algorithm was used to acquire the optimal fuzzy rules for the provided fitness function. Those fuzzy rules were composed of different thresholds for different test sites. At site B, for example, eight rules were acquired by training the F-ABC. Among these were four rules for water and four rules for building shadow. Similar to combining fuzzy classification with other swarm algorithms such as ACO and PSO, the first rule for each class covers the greatest number of training samples; this indicated that the first rule had the most capability to describe characteristics related to its own class. The specific thresholds of first rules are shown in Table
The extraction rules of water and shadow.
Rule | Threshold | B1 | B2 | B3 | B4 | B5 | B6 | B7 | B8 | Brt | Std | NUMPIX | ROUND | LWR |
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Water | Max | 578.2 | 696.1 | 652.9 | 170.0 | 1.133 | 1.333 | 3.515 | 0.198 | 598.8 | 127.2 | 73345 | 0.64 | 15 |
Min | 180.6 | 166.3 | 117.3 | 63.39 | 0.559 | 0.879 | 2.252 | −0.556 | 147.9 | 6.625 | 19 | 0.02 | 1 | |
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Shadow | Max | 578.2 | 224.6 | 212.0 | 213.7 | 1.01 | 1.333 | 3.515 | 0.183 | 598.8 | 343.2 | 28616 | 0.60 | 2.96 |
Min | 180.6 | 166.3 | 117.3 | 95.7 | 0.559 | 0.817 | 0.676 | −0.556 | 147.9 | 15.1 | 19 | 0.08 | 1 |
Figure
Comparison of normalized smooth curves based on fuzzy rules for water and shadow.
To address the problem of water features being confused with building shadow and so being misclassified in urban areas, we proposed an object-based water and building shadow detection method, namely, F-ABC, which is based on fuzzy-rules classification and ABC optimization. First, multiscale segmentation was applied to high-resolution imagery and the best suitable scale was selected by visual interpretation. Four spectral ratio bands were calculated as additional input parameters for improving the accuracy of the segmentation result. Second, image segmentation was employed to calculate object’s statistical features, which included single-band gray value, ratio of multibands, and geometrical characteristics. Then, using the geometric mean (
The experiment was carried out on two test sites exposed to GF-1 imagery in Xiamen City and the result of F-ABC was compared to that of the conventional SVM method. By integrating visual interpretation and statistical results, we analyzed the extraction rules and synthetically estimated the accuracy of applying the proposed method in urban water extraction. Compared with outputs of the widely used SVM method, our results showed that F-ABC improved the overall accuracy of extraction from approximately 6% to 15% and the kappa coefficient values from approximately 0.1 to 0.2. The results indicate that the proposed method could effectively distinguish water from building shadow; this could satisfy actual needs.
Further analysis of the extraction rules indicated that although the two types of objects are similar in most features, the NIR band, red/NIR band, and the length-width-ratio band are significantly influenced by the differences between water and building shadows. Follow-up research would combine these results with another index model, such as the SWI (shadow water index) [
The authors declare that there are no competing interests.
This work was supported in part by the National Natural Science Foundation of China under Grants 41401475, 41471366, and 41501448 and Xiamen University of Technology high level talents under Grant YKJ13022R. The study was in cooperation with Universities Project of Fujian Bureau of Surveying, Mapping and Geoinformation (no. 2015JX04).