scholarly journals Progress Guidance Representation for Robust Interactive Extraction of Buildings from Remotely Sensed Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5111
Author(s):  
Zhen Shu ◽  
Xiangyun Hu ◽  
Hengming Dai
Keyword(s):  
Remote Sensing ◽  
Automatic Segmentation ◽  
Remotely Sensed ◽  
Natural Image ◽  
Building Extraction ◽  
Interactive Segmentation ◽  
Training Scheme ◽  
Remotely Sensed Images ◽  
Cadastral Surveying ◽  
The Stability

Accurate building extraction from remotely sensed images is essential for topographic mapping, cadastral surveying and many other applications. Fully automatic segmentation methods still remain a great challenge due to the poor generalization ability and the inaccurate segmentation results. In this work, we are committed to robust click-based interactive building extraction in remote sensing imagery. We argue that stability is vital to an interactive segmentation system, and we observe that the distance of the newly added click to the boundaries of the previous segmentation mask contains progress guidance information of the interactive segmentation process. To promote the robustness of the interactive segmentation, we exploit this information with the previous segmentation mask, positive and negative clicks to form a progress guidance map, and feed it to a convolutional neural network (CNN) with the original RGB image, we name the network as PGR-Net. In addition, an adaptive zoom-in strategy and an iterative training scheme are proposed to further promote the stability of PGR-Net. Compared with the latest methods FCA and f-BRS, the proposed PGR-Net basically requires 1–2 fewer clicks to achieve the same segmentation results. Comprehensive experiments have demonstrated that the PGR-Net outperforms related state-of-the-art methods on five natural image datasets and three building datasets of remote sensing images.

scholarly journals Semantic Segmentation on Remotely-Sensed Images Using Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning

2018 ◽  
Author(s):  
Teerapong Panboonyuen ◽  
Kulsawasd Jitkajornwanich ◽  
Siam Lawawirojwong ◽  
Panu Srestasathiern ◽  
Peerapon Vateekul
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Remotely Sensed ◽  
Convolutional Network ◽  
Domain Specific ◽  
Medium Resolution ◽  
Remotely Sensed Images ◽  
Very High ◽  
Resolution Data

In remote sensing domain, it is crucial to automatically annotate semantics, e.g., river, building, forest, etc, on the raster images. Deep Convolutional Encoder Decoder (DCED) network is the state-of-the-art semantic segmentation for remotely-sensed images. However, the accuracy is still limited, since the network is not designed for remotely sensed images and the training data in this domain is deficient. In this paper, we aim to propose a novel CNN network for semantic segmentation particularly for remote sensing corpora with three main contributions. First, we propose to apply a recent CNN network call ''Global Convolutional Network (GCN)'', since it can capture different resolutions by extracting multi-scale features from different stages of the network. Also, we further enhance the network by improving its backbone using larger numbers of layers, which is suitable for medium resolution remotely sensed images. Second, ''Channel Attention'' is presented into our network in order to select most discriminative filters (features). Third, ''Domain Specific Transfer Learning'' is introduced to alleviate the scarcity issue by utilizing other remotely sensed corpora with different resolutions as pre-trained data. The experiment was then conducted on two given data sets: ($i$) medium resolution data collected from Landsat-8 satellite and ($ii$) very high resolution data called ''ISPRS Vaihingen Challenge Data Set''. The results show that our networks outperformed DCED in terms of $F1$ for 17.48% and 2.49% on medium and very high resolution corpora, respectively.

scholarly journals Semantic Segmentation on Remotely-Sensed Images Using Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning

2018 ◽  
Author(s):  
Teerapong Panboonyuen ◽  
Kulsawasd Jitkajornwanich ◽  
Siam Lawawirojwong ◽  
Panu Srestasathiern ◽  
Peerapon Vateekul
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Remotely Sensed ◽  
Convolutional Network ◽  
Domain Specific ◽  
Medium Resolution ◽  
Remotely Sensed Images ◽  
Very High ◽  
Resolution Data

In remote sensing domain, it is crucial to annotate semantics, e.g., river, building, forest, etc, on the raster images. Deep Convolutional Encoder Decoder (DCED) network is the state-of-the-art semantic segmentation for remotely-sensed images. However, the accuracy is still limited, since the network is not designed for remotely sensed images and the training data in this domain is deficient. In this paper, we aim to propose a novel CNN for semantic segmentation particularly for remote sensing corpora with three main contributions. First, we propose to apply a recent CNN call ``Global Convolutional Network (GCN)'', since it can capture different resolutions by extracting multi-scale features from different stages of the network. Also, we further enhance the network by improving its backbone using larger numbers of layers, which is suitable for medium resolution remotely sensed images. Second, ``Channel Attention'' is presented into our network in order to select most discriminative filters (features). Third, ``Domain Specific Transfer Learning'' is introduced to alleviate the scarcity issue by utilizing other remotely sensed corpora with different resolutions as pre-trained data. The experiment was then conducted on two given data sets: ($i$) medium resolution data collected from Landsat-8 satellite and ($ii$) very high resolution data called ``ISPRS Vaihingen Challenge Data Set''. The results show that our networks outperformed DCED in terms of $F1$ for 17.48% and 2.49% on medium and very high resolution corpora, respectively.

scholarly journals Making Low-Resolution Satellite Images Reborn: A Deep Learning Approach for Super-Resolution Building Extraction

2021 ◽  
Vol 13 (15) ◽  
pp. 2872
Author(s):  
Lixian Zhang ◽  
Runmin Dong ◽  
Shuai Yuan ◽  
Weijia Li ◽  
Juepeng Zheng ◽  
...  
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Remotely Sensed ◽  
Extraction Methods ◽  
Building Extraction ◽  
Low Resolution ◽  
Existing Building ◽  
Remotely Sensed Images ◽  
The Given ◽  
Low Resolution Images

Existing methods for building extraction from remotely sensed images strongly rely on aerial or satellite-based images with very high resolution, which are usually limited by spatiotemporally accessibility and cost. In contrast, relatively low-resolution images have better spatial and temporal availability but cannot directly contribute to fine- and/or high-resolution building extraction. In this paper, based on image super-resolution and segmentation techniques, we propose a two-stage framework (SRBuildingSeg) for achieving super-resolution (SR) building extraction using relatively low-resolution remotely sensed images. SRBuildingSeg can fully utilize inherent information from the given low-resolution images to achieve high-resolution building extraction. In contrast to the existing building extraction methods, we first utilize an internal pairs generation module (IPG) to obtain SR training datasets from the given low-resolution images and an edge-aware super-resolution module (EASR) to improve the perceptional features, following the dual-encoder building segmentation module (DES). Both qualitative and quantitative experimental results demonstrate that our proposed approach is capable of achieving high-resolution (e.g., 0.5 m) building extraction results at 2×, 4× and 8× SR. Our approach outperforms eight other methods with respect to the extraction result of mean Intersection over Union (mIoU) values by a ratio of 9.38%, 8.20%, and 7.89% with SR ratio factors of 2, 4, and 8, respectively. The results indicate that the edges and borders reconstructed in super-resolved images serve a pivotal role in subsequent building extraction and reveal the potential of the proposed approach to achieve super-resolution building extraction.

scholarly journals Making Low-Resolution Satellite Images Reborn: A Deep Learning Approach for Super-Resolution Building Extraction

2021 ◽  
Author(s):  
Lixian Zhang ◽  
Runmin Dong ◽  
Shuai Yuan ◽  
Weijia Li ◽  
Juepeng Zheng ◽  
...  
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Remotely Sensed ◽  
Extraction Methods ◽  
Building Extraction ◽  
Low Resolution ◽  
Existing Building ◽  
Remotely Sensed Images ◽  
The Given ◽  
Low Resolution Images

Existing methods for building extraction from remotely sensed images strongly rely on aerial or satellite-based images with very high resolution, which are usually limited by spatiotemporally accessibility and cost. In contrast, relatively low-resolution images have better spatial and temporal availability but cannot directly contribute to fine- and/or high-resolution building extraction. In this paper, based on image super-resolution and segmentation techniques, we propose a two-stage framework (SRBuildingSeg) for achieving super-resolution (SR) building extraction using relatively low-resolution remotely sensed images. SRBuildingSeg can fully utilize inherent information from the given low-resolution images to achieve high-resolution building extraction. In contrast to the existing building extraction methods, we first utilize an internal pairs generation module (IPG) to obtain SR training datasets from the given low-resolution images and an edge-aware super-resolution module (EASR) to improve the perceptional features, following the dual-encoder building segmentation module (DES). Both qualitative and quantitative experimental results demonstrate that our proposed approach is capable of achieving high-resolution (e.g. 0.5 m) building extraction results at 2×, 4× and 8× SR. Our approach outperforms 8 other methods with respect to the extraction result of mean Intersection over Union (mIoU) values by a ratio of 9.38%, 8.20% and 7.89% with SR ratio factors of 2, 4, and 8, respectively. The results indicate that the edges and borders reconstructed in super-resolved images serve a pivotal role in subsequent building extraction and reveal the potential of the proposed approach to achieve super-resolution building extraction. Our code is available at https://github.com/xian1234/SRBuildSeg.

scholarly journals Remotely Sensed Image Classification by Complex Network Eigenvalue and Connected Degree

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Mengxi Xu ◽  
Chenglin Wei
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Complex Network ◽  
Clustering Algorithm ◽  
Remote Sensing Image ◽  
Remotely Sensed ◽  
Clustering Coefficient ◽  
Classification Method ◽  
Remotely Sensed Image ◽  
Remotely Sensed Images

It is a well-known problem of remotely sensed images classification due to its complexity. This paper proposes a remotely sensed image classification method based on weighted complex network clustering using the traditionalK-means clustering algorithm. First, the degree of complex network and clustering coefficient of weighted feature are used to extract the features of the remote sensing image. Then, the integrated features of remote sensing image are combined to be used as the basis of classification. Finally,K-means algorithm is used to classify the remotely sensed images. The advantage of the proposed classification method lies in obtaining better clustering centers. The experimental results show that the proposed method gives an increase of 8% in accuracy compared with the traditionalK-means algorithm and the Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm.

Remote Sensing and Spatial Statistics as Tools in Crime Analysis

2005 ◽  
pp. 270-292 ◽  
Author(s):  
Dongmei Chen ◽  
John R. Weeks ◽  
John V. Kaiser Jr.
Keyword(s):  
Remote Sensing ◽  
Spatial Statistics ◽  
Analytical Approach ◽  
Spatial Information ◽  
Criminal Activity ◽  
Emergency Situation ◽  
Remotely Sensed ◽  
Remotely Sensed Images ◽  
Basic Approaches ◽  
New Variables

This chapter explores the feasibility and utility of using aerial photography or remotely sensed satellite imagery to identify geographic or “place” features that may be associated with criminal activity. It assesses whether or not variables derived from satellite images can provide surrogate relationships between land use and crime. A review of the remote sensing literature suggests two basic approaches to the use of remotely sensed images in law enforcement: (1) tactical; and (2) analytical. The tactical approach uses the imagery as a background to the maps and other spatial information that an officer on the beat might have as he or she is investigating a crime or emergency situation. The analytical approach uses the remotely sensed images to create new variables that may serve as proxies for the risk of crime in particular locations. In this study we employ the analytical approach to the use of remotely sensed images, classifying images according to the presence or absence of vegetation within a pixel, as well as the classification of specific urban attributes, such as parking lots. We also employ spatial statistics to quantify the relationship between features of the images and crime events on the ground, and these analyses may be particularly useful as input to policy decisions about policing within the community.

scholarly journals Road Segmentation on Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields

2017 ◽  
Author(s):  
Teerapong Panboonyuen ◽  
Peerapon Vateekul ◽  
Kulsawasd Jitkajornwanich ◽  
Siam Lawawirojwong ◽  
Panu Srestasathiern
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Landscape Metrics ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Object Segmentation ◽  
Remotely Sensed ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Remotely Sensed Images

Object segmentation on remotely-sensed images: aerial (or very high resolution, VHS) images and satellite (or high resolution, HR) images, has been applied to many application domains, especially road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts in applying deep convolutional neural network (DCNN) to extract roads from remote sensing images have been made; however, the accuracy is still limited. In this paper, we present an enhanced DCNN framework specifically tailored for road extraction on remote sensing images by applying landscape metrics (LMs) and conditional random fields (CRFs). To improve DCNN, a modern activation function, called exponential linear unit (ELU), is employed in our network resulting in a higher number of and yet more accurate extracted roads. To further reduce falsely classified road objects, a solution based on an adoption of LMs is proposed. Finally, to sharpen the extracted roads, a CRF method is added to our framework. The experiments were conducted on Massachusetts road aerial imagery as well as THEOS satellite imagery data sets. The results showed that our proposed framework outperformed Segnet, the state-of-the-art object segmentation technique on any kinds of remote sensing imagery, in most of the cases in terms of precision, recall, and F1.

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