text: 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.
  @cite_14: Three-dimensional realistic representations of buildings in urban environments have been increasingly applied as data sources in a growing number of remote sensing fields such as urban planning and city management, navigation, environmental simulation (i.e. flood, earthquake, air pollution), 3D change detection after events like natural disasters or conflicts, etc. With recent technological developments, it becomes possible to acquire high-quality 3D input data. There are two main ways to obtain elevation information: from active remote sensing systems, such as light detection and ranging (LIDAR), and from passive remote sensing systems, such as optical images, which allow the acquisition of stereo images for automatic digital surface models (DSMs) generation. Although airborne laser scanning provides very accurate DSMs, it is a costly method. On the other hand, the DSMs from stereo satellite imagery show a large coverage and lower costs. However, they are not as accurate as LIDAR DSMs. With respect to automatic 3D information extraction, the availability of accurate and detailed DSMs is a crucial issue for automatic 3D building model reconstruction. We present a novel methodology for generating a better-quality stereo DSM with refined buildings shapes using a deep learning framework. To this end, a conditional generative adversarial network (cGAN) is trained to generate accurate LIDAR DSM-like height images from noisy stereo DSMs.
  @cite_4: 
  @cite_28: We propose a novel building detection algorithm for processing high-resolution aerial images. Our algorithm exploits the building-shadow geometric relationship according to lighting models, making it suitable to detect buildings in a more general setting, possibly with irregular shapes. We use image segmentation to provide spatial support for both building and shadow detections. A novel confidence method is developed to label building and shadow segments by jointly reasoning: 1) the likelihood of shadows; 2) building-shadow configuration, and 3) building-building similarity. Our method is tested on a wide range of aerial images. Qualitative and quantitative results demonstrate its effectiveness on detecting and extracting buildings from background clutter.
  @cite_21: A building detection method based on multi-spectral image and airborne laser scanning data is presented. Detecting building footprints from laser points cloud data is one of the most difficult problems in building modeling and edge detection. Unbalanced support vector machine can be used to classify laser points from their features, and solve the classification problems of unbalanced dataset with dense buildings and differentiate neighboring tree points from building points. The features include height, height variation information from LIDAR data and spectrum information from registered aerial image. Firstly, candidate building points were selected, and then standard support vector machines were used for preliminary training. The information provided by projected normal difference of training points in normal vector and training size were used to determine the penalty factors of two classes. At last, a new classification hyperplane could be obtained by training again. The experiments show that the classification method based on unbalanced support vector machines can efficiently detect the building points from LIDAR data, and improve the classification accuracy of unbalanced dataset.
  @cite_6: 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.
  @cite_15: We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity.
  @cite_34: 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.
  @cite_10: 
  @cite_17: The intermediate map responses of a Convolutional Neural Network (CNN) contain contextual knowledge about its input. In this paper, we present a framework that uses these activation maps from several layers of a CNN as features to a Deep Belief Network (DBN) using transfer learning to provide an understanding of an input image. We create a representation of these features and the training data and use them to extract more information from an image at the pixel level, hence gaining understanding of the whole image. We experimentally demonstrate the usefulness of our framework using a pretrained model and use a DBN to perform segmentation on the BAERI dataset of Synthetic Aperture Radar (SAR) imagery and the CAMVID dataset with a relatively smaller training dataset.
summary: 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