[논문]Automatic Classification of Drone Images Using Deep Learning and SVM with Multiple Grid Sizes

Kim, Sun Woong; Kang, Min Soo; Song, Junyoung; Park, Wan Yong; Eo, Yang Dam; Pyeon, Mu Wook

doi:10.7848/ksgpc.2020.38.5.407

Automatic Classification of Drone Images Using Deep Learning and SVM with Multiple Grid Sizes 원문보기

한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.38 no.5, 2020년, pp.407 - 414

Kim, Sun Woong (Department of Advanced Technology Fusion, Konkuk University) , Kang, Min Soo (Jigusoft Inc) , Song, Junyoung (Department of Civil and Environmental Engineering, Konkuk University) , Park, Wan Yong (Agency for Defense Development) , Eo, Yang Dam (Department of Civil and Environmental Engineering, Konkuk University) , Pyeon, Mu Wook (Department of Civil and Environmental Engineering, Konkuk University)

Abstract ▼ AI-Helper

SVM (Support vector machine) analysis was performed after applying a deep learning technique based on an Inception-based model (GoogLeNet). The accuracy of automatic image classification was analyzed using an SVM with multiple virtual grid sizes. Six classes were selected from a standard land cover map. Cars were added as a separate item to increase the classification accuracy of roads. The virtual grid size was 2-5 m for natural areas, 5-10 m for traffic areas, and 10-15 m for building areas, based on the size of items and the resolution of input images. The results demonstrate that automatic classification accuracy can be increased by adopting an integrated approach that utilizes weighted virtual grid sizes for different classes.

주제어

표/그림 (11)

그림 Fig. 1 . Spatial model developed using ERDAS spatial modeler
그림 Fig. 2 . Experimental area
그림 Fig. 3. Drone images used as training data
그림 Fig. 4 . Classification results for a grid size of 13.1 m
그림 Fig. 5 . Classification results for a grid size of 6.55 m
그림 Fig. 6 . Classification results for a grid size of 4.36 m
표 Table 1 . Mapping between classification categories and land cover map codes
그림 Fig. 7. Grid classification map (6.55 m)
표 Table 2 . Classification error matrix for grid size of 13.1 m
표 Table 4 . Classification error matrix for grid size of 4.36 m
표 Table 5 . Classification error matrix for integrated approach

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문제 정의

Notley & Magdon-Ismail (2018) extracted features from images and numeric data and used them as inputs for SVMs and KNN (k-nearest neighbor) classifiers to determine if neuralnetwork- extracted features enhanced the capabilities of these models. This study explores the idea of replacing the typical softmax classier in a neural network with an SVM or a KNN classifier. The results of this study indicate that combining the features derived from neural networks with alternate classification models can provide high classification accuracy.

제안 방법

As a method of creating land cover maps for the Ministry of Environment, this paper proposes an efficient process for extracting features from a CNN and determining classes from drone images using a SVM (support vector machine). As the land cover map classification system of the Ministry of Environment entails the attribute of land use (artificial classification with regard to human activities), it is expected that methods that perform classification based on various features, such as deep learning, will produce better results compared to a basic classification method based on pixel value distributions or textures.
Results showed that the predicted classification probability and actual classification accuracy did not agree for a single size of the virtual grid. Classification accuracy was improved using an integrated approach with weighted virtual grid sizes for each class. Classification accuracy was less than 70%.
For reclassification, the maximum method in Mosaic operator at Mosaic tool of ArcGIS was utilized to assign priority to the pixels that overlapped with the new raster merging task in the mosaic. Further, as a preprocessing method, the data were reclassified and labeled as follows: villa/building = 2, car/road = 1, tree/ forest = 3, and the remaining content excluding the main content = 0.
The model was trained with the abovementioned six classes to clearly determine the classification performance of the target area among all layers. Further, the model was additional trained on the car class to increase the accuracy of classifying roads.
The experiment was conducted according to the flowchart shown in Fig. 1, and the sizes of the virtual grid were configured to be similar to the average size of each class of self-learning data. The sizes of the classification grid were recommended by the ERDAS autogrid function as 10–15 m each for villas and buildings (residential areas), 5–10 m each for roads and cars (traffic areas), and 2–5 m each for natural and man-made vegetation (grass field areas).
The items for the classification of images were selected based on the level-2 land cover map. The model was trained with the abovementioned six classes to clearly determine the classification performance of the target area among all layers. Further, the model was additional trained on the car class to increase the accuracy of classifying roads.
To conduct a one-to-one quantitative comparison, the land cover map was reclassified into three categories—traffic area, natural field area, and building area—in accordance with the grid classification criteria, as summarized in Table 1.

대상 데이터

The area around Shingu College in Seongnam-si, Gyeonggido, was selected as the experimental location because drone images are available for this area and it is characterized by an even distribution of residential, commercial, traffic, man-made grass field, and natural grass field areas. The images used in the experiment were obtained in 2017 at a ground sampling distance of 5 cm by a drone equipped with a senseFly camera sensor. WPS (Web processing service) model was constructed using a spatial modeler in ERDAS IMAGINE.
The “Training Image Chips Location” stage involves generating a library for self-learning. The library comprises 60 images for each class among the six classes. This generated library is sent to the Inception stage.

성능/효과

Classification accuracy was improved using an integrated approach with weighted virtual grid sizes for each class. Classification accuracy was less than 70%. A few characteristics of buildings area were lost while preparing the initial deep learning data for a grid size of 4.
Labeling was carried out manually, whereas image processing and normalization were performed automatically. Results showed a classification accuracy of 98%. Kim et al.
(2019) used Tiny-YOLO-V2 and Faster R-CNN to detect and analyze cracks on the surface of paved roads. Results showed that the Faster R-CNN algorithm was more effective at this task than the YOLO algorithm.
This study explores the idea of replacing the typical softmax classier in a neural network with an SVM or a KNN classifier. The results of this study indicate that combining the features derived from neural networks with alternate classification models can provide high classification accuracy.

후속연구

Further studies are required for determining the priorities corresponding to different virtual grid sizes and method for expressing the classification results with a fixed grid size.

참고문헌 (16)

Song, A.R. (2019), A Novel Deep learning Framework for Multi-Class Change Detection of Hyperspectral Images, Ph.D. Dissertation, Seoul National University, 13p. (in Korean with English abstract)
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Joo, Y.D. (2017), Drone Image Classification based on Convolutional Neural Networks, The Journal of The Institute of Internet, Broadcasting and Communication (IIBC) , Vol. 17, No. 5, pp.97-102. (in Korean with English abstract)
Jo, H.J. (2017), A Study on Image Acquisition and Image Processing using Drones, Master thesis, Kyungpook University, 83p. (in Korean with English abstract)
Bengio, Y. (2011), Deep learning of representations for unsupervised and transfer learning, Proceeding of ICML workshop on unsupervised and transfer learning-2011, 02 July, Bellevue, Washington, USA Vol. 27, pp.17-36.
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Girshick, R. (2015), Fast r-cnn, IEEE conference on computer vision and pattern recognition - 2015, 07-13 Dec, Santiago, Chile, pp.1440-1448.
Kim, J.M., Hyeon. S.G., Chae, J.H., and Do, M.S. (2019), Road Crack Detection based on Object Detection Algorithm, using Unmanned Aerial Vehicle Image, J. Korea Inst. Intell. Transp. Syst, Vol.18 No.6, pp.155-163. (in Korean with English abstract)

원문보기 상세보기
Kang, N.Y., Pak, J.G., Cho, G.S. and Yeu, Y. (2012), An Analysis of Land Cover Classification Methods Using IKONOS Satellite Image, Journal of Korean Society for Geospatial Information Science , Vol.20 No.3, p p.65-71. (in Korean with English abstract)
Ham, S.W. (2019), Semantic Segmentation of Drone Images Using Deep Learning - Focusing on Illegal Building Monitoring-, Master's Thesis at University of Seoul, 17p. (in Korean with English abstract)
Christian, S., Wei, L., Yangqing, J., Pierre, S., Scott, R., Dragomir, A., Dumitru, E., Vincent, V., Andrew, R. (2015), Going Deeper with Convolutions, The proceeding of CVPR : 28th IEEE Conference on Computer Vision and Pattern Recognition-2015, 7-12 June, Boston, MA, USA, pp.1-9.
Tang, Y. (2013), Deep Learning using Linear Support Vector Machines, International Conference on Machine Learning: Challenges in Representation Learning Workshop-2013.
Ren,S., He, K., Girshick, R. and Sun, J. (2015), Faster R-CNN: Towards Real-Time Object, Detection with Region Proposal Networks, Advances in Neural Information Processing Systems 28 Proceeding- 2015, Dec, Montreal CANADA. pp. 91-99.
Peng, C.-Y. J., Lee, K. L. and Gary, M.Ingersoll. (2002), An Introduction to Logistic Regression Analysis and Reporting, The Journal of Educational Research. Vol.96 No.1, pp.3-14.

상세보기
Biserka Petrovska, Eftim Zdravevski, Petre Lameski, Roberto Corizzo, Ivan Stajduhar and Jonatan Lerga, (2020), Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification, Sensors, 20(14), 3906; doi:10.3390/s20143906

상세보기
Notley, S., Magdon-Ismail, M., (2018), Examining the use of neural networks for feature extraction: A comparative analysis using deep learning, support vector machines, and k-nearest neighbor classifiers. arXiv preprint arXiv:1805.02294.

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