scholarly journals A Framework for Automatic Building Detection from Low-Contrast Satellite Images

Symmetry ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 3 ◽  
Author(s):  
Muhammad Aamir ◽  
Yi-Fei Pu ◽  
Ziaur Rahman ◽  
Muhammad Tahir ◽  
Hamad Naeem ◽  
...  
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Satellite Images ◽  
Discrete Wavelet ◽  
Building Detection ◽  
Detection Scheme ◽  
Low Contrast ◽  
Line Segments ◽  
High Resolution Satellite Images ◽  
Wide Range

Building detection in satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve building detection performance. Different algorithms have been proposed to extract building objects from high-resolution satellite images with standard contrast. However, building detection from low-contrast satellite images to predict symmetrical findings as of past studies using normal contrast images is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to detect buildings from low-contrast satellite images. In an effort to enhance visualization of satellite images, in this study, first, the contrast of an image is optimized to represent all the information using singular value decomposition (SVD) based on the discrete wavelet transform (DWT). Second, a line-segment detection scheme is applied to accurately detect building line segments. Third, the detected line segments are hierarchically grouped to recognize the relationship of identified line segments, and the complete contours of the building are attained to obtain candidate rectangular buildings. In this paper, the results from the method above are compared with existing approaches based on high-resolution images with reasonable contrast. The proposed method achieves high performance thus yields more diversified and insightful results over conventional techniques.

scholarly journals Urban areas analysis using satellite image segmentation and deep neural network

2019 ◽  
Vol 135 ◽  
pp. 01064
Author(s):  
Vladimir Khryaschev ◽  
Leonid Ivanovsky
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Urban Areas ◽  
High Performance ◽  
Satellite Images ◽  
Satellite Image ◽  
Aerial Images ◽  
Building Detection ◽  
High Resolution Satellite Images

The goal of our research was to develop methods based on convolutional neural networks for automatically extracting the locations of buildings from high-resolution aerial images. To analyze the quality of developed deep learning algorithms, there was used Sorensen-Dice coefficient of similarity which compares results of algorithms with real masks. These masks were generated automatically from json files and sliced on smaller parts together with respective aerial photos before the training of developed convolutional neural networks. This approach allows us to cope with the problem of segmentation for high-resolution satellite images. All in all we show how deep neural networks implemented and launched on modern GPUs of high-performance supercomputer NVIDIA DGX-1 can be used to efficiently learn and detect needed objects. The problem of building detection on satellite images can be put into practice for urban planning, building control of some municipal objects, search of the best locations for future outlets etc.

scholarly journals SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images

2021 ◽  
Vol 7 ◽  
pp. e772
Author(s):  
Ahmed I. Shahin ◽  
Sultan Almotairi
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Visual Attention ◽  
Satellite Images ◽  
Boundary Detection ◽  
Single Shot ◽  
Building Detection ◽  
Desert Environment ◽  
Low Contrast ◽  
High Resolution Satellite Images

Building detection in high-resolution satellite images has received great attention, as it is important to increase the accuracy of urban planning. The building boundary detection in the desert environment is a real challenge due to the nature of low contrast images in the desert environment. The traditional computer vision algorithms for building boundary detection lack scalability, robustness, and accuracy. On the other hand, deep learning detection algorithms have not been applied to such low contrast satellite images. So, there is a real need to employ deep learning algorithms for building detection tasks in low contrast high-resolution images. In this paper, we propose a novel building detection method based on a single-shot multi-box (SSD) detector. We develop the state-of-the-art SSD detection algorithm based on three approaches. First, we propose data-augmentation techniques to overcome the low contrast images’ appearance. Second, we develop the SSD backbone using a novel saliency visual attention mechanism. Moreover, we investigate several pre-trained networks performance and several fusion functions to increase the performance of the SSD backbone. The third approach is based on optimizing the anchor-boxes sizes which are used in the detection stage to increase the performance of the SSD head. During our experiments, we have prepared a new dataset for buildings inside Riyadh City, Saudi Arabia that consists of 3878 buildings. We have compared our proposed approach vs other approaches in the literature. The proposed system has achieved the highest average precision, recall, F1-score, and IOU performance. Our proposed method has achieved a fast average prediction time with the lowest variance for our testing set. Our experimental results are very promising and can be generalized to other object detection tasks in low contrast images.

scholarly journals Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data

2019 ◽  
Vol 11 (4) ◽  
pp. 403 ◽  
Author(s):  
Weijia Li ◽  
Conghui He ◽  
Jiarui Fang ◽  
Juepeng Zheng ◽  
Haohuan Fu ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Data Augmentation ◽  
Semantic Segmentation ◽  
Building Detection ◽  
Building Extraction ◽  
Three Solutions ◽  
High Resolution Satellite Images ◽  
Building Footprint ◽  
Map Data

Automatic extraction of building footprints from high-resolution satellite imagery has become an important and challenging research issue receiving greater attention. Many recent studies have explored different deep learning-based semantic segmentation methods for improving the accuracy of building extraction. Although they record substantial land cover and land use information (e.g., buildings, roads, water, etc.), public geographic information system (GIS) map datasets have rarely been utilized to improve building extraction results in existing studies. In this research, we propose a U-Net-based semantic segmentation method for the extraction of building footprints from high-resolution multispectral satellite images using the SpaceNet building dataset provided in the DeepGlobe Satellite Challenge of IEEE Conference on Computer Vision and Pattern Recognition 2018 (CVPR 2018). We explore the potential of multiple public GIS map datasets (OpenStreetMap, Google Maps, and MapWorld) through integration with the WorldView-3 satellite datasets in four cities (Las Vegas, Paris, Shanghai, and Khartoum). Several strategies are designed and combined with the U-Net–based semantic segmentation model, including data augmentation, post-processing, and integration of the GIS map data and satellite images. The proposed method achieves a total F1-score of 0.704, which is an improvement of 1.1% to 12.5% compared with the top three solutions in the SpaceNet Building Detection Competition and 3.0% to 9.2% compared with the standard U-Net–based method. Moreover, the effect of each proposed strategy and the possible reasons for the building footprint extraction results are analyzed substantially considering the actual situation of the four cities.

Sign in / Sign up

Export Citation Format

Share Document