Abstract
Convolutional neural network (CNN) has shown great success in computer vision tasks, but their application in land-use type classifications within the context of object-based image analysis has been rarely explored, especially in terms of the identification of irregular segmentation objects. Thus, a blocks-based object-based image classification (BOBIC) method was proposed to carry out end-to-end classification for segmentation objects using CNN. Specifically, BOBIC takes advantage of CNN to automatically extract complex features from the original image data, thereby avoiding the uncertainty caused by the manual extraction of features in OBIC. Additionally, OBIC compensates for the shortcomings of CNN whereby it is difficult to delineate a clear right boundary for ground objects at the pixel level. Using three high-resolution test images, the proposed BOBIC was compared with support vector machine (SVM) and random forest (RF) classifiers, and then, the effect of image blocks and mixed objects on classification accuracy was evaluated for the proposed BOBIC. Compared with conventional SVM and RF classifiers, the inclusion of CNN improved the OBIC classification performance substantially (5% to 10% increases in overall accuracy), and it also alleviated the effect derived from mixed objects.
Highlights
Object identification in very high-resolution (VHR) remote sensing imagery has always been a fundamental but challenging issue
A blocks-based OBIC (BOBIC) method was proposed for applying a Convolutional neural network (CNN) to OBIC
Compared with traditional classification methods, the proposed method utilizes the ability of CNN to automatically extract high-level features, thereby achieving end-to-end classification for irregular segmentation objects within the framework of object-based image analysis (OBIA)
Summary
Object identification in very high-resolution (VHR) remote sensing imagery has always been a fundamental but challenging issue. In the past few decades, various methods for the identification of different types of objects have been proposed, including the template matching-based method,[1,2,3] knowledge-based method,[4,5,6] object-based image analysis (OBIA) method,[7,8,9] and machine learning-based method.[10,11] Among them, the OBIA method can be combined with geographical information system (GIS) techniques, which allows for more complete mapping of land-use types for GIS analyses.[12] OBIA has attracted the attention of many scholars.[12,13,14] The first step in OBIA is to segment the images into relatively homogeneous regions (segmentation objects),[15] and the statistical information for the segmentation objects is employed for image analyses (e.g., object-based image classification, hereafter, OBIC). The segmented objects exhibit rich spectral and textural features, and provide shape and contextual information,[16] which can improve the classification performance for various types of objects
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