Machine learning has proven to be useful in classification and segmentation of images. In this paper, we evaluate a training methodology for pixel-wise segmentation on high resolution satellite images using progressive growing of generative adversarial networks. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We present our findings using the SpaceNet version 2 dataset. Progressive GAN training achieved a test accuracy of 93 compared to 89 for traditional GAN training.
Significant accomplishments have been made in computer vision, resulting in increasingly effective state of the art methods for image processing @cite_17 @cite_15 . Early efforts in automatic rooftop segmentation relied on techniques to generate candidate rooftops and subsequent evaluation to accept or reject candidate rooftops. Edge detection, corner detection, and segmentation into homogeneous regions via k-means clustering or support Vector Machines (SVM) have been used to identify candidate rooftops @cite_6 @cite_34 . Discriminative features used to evaluate candidate rooftops include building shadows, geometry and spectral characteristics @cite_28 @cite_4 . Several approaches have used LIDAR alone or in addition to multi-spectral images @cite_21 @cite_10 @cite_14
@cite_14 @cite_4 @cite_28 @cite_21 @cite_6 @cite_15 @cite_34 @cite_10 @cite_17
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This paper presents a novel application of machine learning techniques to the automatic detection of building rooftops in satellite images. The image is first segmented into homogeneous regions using the k-means algorithm. These segments are then treated as candidate rooftop regions which are presented to a novel two-stage classification process, features are extracted from each segment and submitted to an ANN which serves as the first stage of the classification procedure. New features are then extracted from the outputs of the ANN and these are presented to an SVM which then performs the second classification pass. In this way, the first classification stage acts as a preprocessing step which, when processed by the SVM significantly reduces the number of false-positives. To establish the efficacy of the proposed method, its results are compared with those obtained using an alternative approach. A new method for detecting rooftops in satellite images is presented. The proposed method is based on a combination of machine learning techniques, namely, k-means clustering and support vector machines (SVM). Firstly k-means clustering is used to segment the image into a set of rooftop candidates—these are homogeneous regions in the image which are potentially associated with rooftop areas. Next, the candidates are submitted to a classification stage which determines which amongst them correspond to “true” rooftops. To achieve improved accuracy, a novel two-pass classification process is used. In the first pass, a trained SVM is used in the normal way to distinguish between rooftop and nonrooftop regions. However, this can be a challenging task, resulting in a relatively high rate of misclassification. Hence, the second pass, which we call the “histogram method,” was devised with the aim of detecting rooftops which were missed in the first pass. The performance of the model is assessed both in terms of the percentage of correctly classified candidates as well as the accuracy of the estimated rooftop area. We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 azimuths, 9 elevations, and 6 lighting conditions was collected (for a total of 194,400 individual images). The objects were 10 instances of 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Five instances of each category were used for training, and the other five for testing. Low-resolution grayscale images of the objects with various amounts of variability and surrounding clutter were used for training and testing. Nearest neighbor methods, support vector machines, and convolutional networks, operating on raw pixels or on PCA-derived features were tested. Test error rates for unseen object instances placed on uniform backgrounds were around 13 for SVM and 7 for convolutional nets. On a segmentation recognition task with highly cluttered images, SVM proved impractical, while convolutional nets yielded 16 7 error. A real-time version of the system was implemented that can detect and classify objects in natural scenes at around 10 frames per second. In this paper, we propose a robust framework for building extraction in visible band images. We first get an initial classification of the pixels based on an unsupervised presegmentation. Then, we develop a novel conditional random field (CRF) formulation to achieve accurate rooftops extraction, which incorporates pixel-level information and segment-level information for the identification of rooftops. Comparing with the commonly used CRF model, a higher order potential defined on segment is added in our model, by exploiting region consistency and shape feature at segment level. Our experiments show that the proposed higher order CRF model outperforms the state-of-the-art methods both at pixel and object levels on rooftops with complex structures and sizes in challenging environments.