Accurate Detection of Built-Up Areas from High-Resolution Remote Sensing Imagery Using a Fully Convolutional Network

Tan, Yihua; Xiong, Shengzhou; Li, Zhi; Tian, Jinwen; Li, Yansheng

doi:10.14358/pers.85.10.737

Cited by 6 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also has higher processing efficiency for the detection of built-up areas in a wide range of images. Recent research has shown that block-based CNN models have significant advantages over traditional hand-crafted feature methods [37], [38], [39]. However, the existing block-based supervised detection still needs to address the following issues.…”

Section: Introductionmentioning

confidence: 99%

Built-Up Area Extraction Combing Densely Connected Dual-Attention Network and Multiscale Context

Chen

Shuai

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Built-up areas are the main areas for human production and life. The wide availability of high-resolution satellite images provides new opportunities for fine-scale detection and mapping of built-up areas. However, great challenges remain because built-up areas are large-scale composite geographical objects, which contain diverse object classes and complex scenes, and have extremely large feature heterogeneity across space. For the automatic recognition of built-up areas in high spatial resolution satellite images, a block-level built-up area extraction framework combing densely connected dual-attention network and multiscale context is proposed. The proposed method first divides an image into multiscale blocks with a certain proportion of overlap through multisize grids and their multistep offsets. It then uses the constructed lightweight network integrating dense connection and dual attention to realize the feature representation and discrimination of image blocks. Finally, it achieves the refined detection of built-up areas by integrating the prediction results under different divisions through pixel-level multilabel voting. GaoFen-2 satellite images covering Shenzhen city, China, are used to verify the effectiveness of the proposed method. In the five selected test areas, the F1 score of the proposed method ranges from 0.8720 to 0.8983. Results visually preserve the integrity of internal morphology and have a well-defined boundary. The proposed method shows better performance than the state-of-the-art built-up area Manuscript

show abstract

Section: Introductionmentioning

confidence: 99%

Built-Up Area Extraction Combing Densely Connected Dual-Attention Network and Multiscale Context

Chen

Shuai

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…A supervised DL model [10], [92], [98], [119] generally requires a massive number of labeled images input for training. Although the CNN model [83], [118] that relied on supervised learning has greatly improved the LUM performance of HSR-RSI. An unsupervised learning approach [120]- [122] that utilizes small amounts of images with no labels is still aroused attention continuously as labeled training samples are not largely available until now.…”

Section: Summary Of Dl-based Lum Methodsmentioning

confidence: 99%

“…So obviously, this kind of approach does not apply the whole image as input, which leads to redundant processing and decrease efficiency when predicting labels of large scale HSR-RSIs. The second segmentation method is based on pixel-to-pixel and end-to-end [33], [98], [118], [156]. It can directly infer pixel-based labels of the whole patch or image.…”

Section: B Pixel-based or Object-basedmentioning

confidence: 99%

Land-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review

Zang

Cao

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Land-use mapping (LUM) using high spatial resolution remote sensing images (HSR-RSIs) is a challenging and crucial technology. However, due to the characteristics of HSR-RSIs, such as different image acquisition conditions and massive, detailed information, performing LUM faces unique scientific challenges. With the emergence of new deep learning (DL) algorithms in recent years, methods to LUM with DL have achieved huge breakthroughs, which offers novel opportunities for the development of LUM for HSR-RSIs. This paper aims to provide a thorough review of recent achievements in this field. Existing high spatial resolution datasets in the research of semantic segmentation and single object segmentation are presented firstly. Next, we introduce several basic DL approaches that are frequently adopted for LUM. After reviewing DLbased LUM methods comprehensively, which highlights the contributions of researchers in the field of LUM for HSR-RSIs, we summarize these DL-based approaches based on two LUM criteria. Individually, the first one has supervised learning, semisupervised learning, or unsupervised learning, while another one is pixel-based or object-based. We then briefly review the fundamentals and the developments of the development of semantic segmentation and single object segmentation. At last, quantitative results that experiment on the dataset of ISPRS Vaihingen and ISPRS Potsdam are given for several representative models such as FCN and U-Net, following up with a comparison and discussion of the results.

show abstract

“…However, due to the effect of image segmentation quality, expressing HR complex scenes is difficult for image objects. Recently, block-based deep learning methods have been applied to extract built-up areas from HR images [12,[35][36][37]. An image block typically contains multiple objects and their spatial distribution patterns.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Multilevel Feature-Fusion Network for Built-Up Area Mapping from Gaofen-2 Satellite Images

Chen,

Peng,

Yao

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The timely, accurate acquisition of geographic spatial information such as the location, scope, and distribution of built-up areas is of great importance for urban planning, management, and decision-making. Due to the diversity of target features and the complexity of spatial layouts, the large-scale mapping of urban built-up areas using high-resolution (HR) satellite imagery still faces considerable challenges. To address this issue, this study adopted a block-based processing strategy and constructed a lightweight multilevel feature-fusion (FF) convolutional neural network for the feature representation and discrimination of built-up areas in HR images. The proposed network consists of three feature extraction modules composed of lightweight convolutions to extract features at different levels, which are further fused sequentially through two attention-based FF modules. Furthermore, to improve the problem of incorrect discrimination and severe jagged boundaries caused by block-based processing, a majority voting method based on a grid offset is adopted to achieve a refined extraction of built-up areas. The effectiveness of this method is evaluated using Gaofen-2 satellite image data covering Shenzhen, China. Compared with several state-of-the-art algorithms for detecting built-up areas, the proposed method achieves a higher detection accuracy and preserves better shape integrity and boundary smoothness in the extracted results.

show abstract

Accurate Detection of Built-Up Areas from High-Resolution Remote Sensing Imagery Using a Fully Convolutional Network

Cited by 6 publications

References 0 publications

Built-Up Area Extraction Combing Densely Connected Dual-Attention Network and Multiscale Context

Built-Up Area Extraction Combing Densely Connected Dual-Attention Network and Multiscale Context

Land-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review

Lightweight Multilevel Feature-Fusion Network for Built-Up Area Mapping from Gaofen-2 Satellite Images

Contact Info

Product

Resources

About