Mapping Impervious Surfaces in Town–Rural Transition Belts Using China’s GF-2 Imagery and Object-Based Deep CNNs

Fu, Yongyong; Liu, Kunkun; Shen, Zhangquan; Deng, Jinsong; Gan, Muye; Liu, Xinguo; Lu, Dongming; Wang, Ke

doi:10.3390/rs11030280

Cited by 34 publications

(32 citation statements)

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“…These networks have the following characteristics: they are fully convolutional networks without fully connection; a skip connection structure combined with deconvolution layers and convolution layers at different depths so as to revert the accurate locations of the geographic objects and add semantic labels to each pixel of the image. The semantic segmentation networks based on CNNs are widely applied in the recognition of buildings [38][39][40][41], the extraction of cadastral boundaries [42] and the land use or land cover change [43,44]. The applications are also expanded to the recognition of the agricultural plants [45], pests and diseases [46,47], especially the Refs.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM

Cui

Meng

et al. 2020

IJGI

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When a landslide happens, it is important to recognize the hazard-affected bodies surrounding the landslide for the risk assessment and emergency rescue. In order to realize the recognition, the spatial relationship between landslides and other geographic objects such as residence, roads and schools needs to be defined. Comparing with semantic segmentation and instance segmentation that can only recognize the geographic objects separately, image captioning can provide richer semantic information including the spatial relationship among these objects. However, the traditional image captioning methods based on RNNs have two main shortcomings: the errors in the prediction process are often accumulated and the location of attention is not always accurate which would lead to misjudgment of risk. To handle these problems, a landslide image interpretation network based on a semantic gate and a bi-temporal long-short term memory network (SG-BiTLSTM) is proposed in this paper. In the SG-BiTLSTM architecture, a U-Net is employed as an encoder to extract features of the images and generate the mask maps of the landslides and other geographic objects. The decoder of this structure consists of two interactive long-short term memory networks (LSTMs) to describe the spatial relationship among these geographic objects so that to further determine the role of the classified geographic objects for identifying the hazard-affected bodies. The purpose of this research is to judge the hazard-affected bodies of the landslide (i.e., buildings and roads) through the SG-BiTLSTM network to provide geographic information support for emergency service. The remote sensing data was taken by Worldview satellite after the Wenchuan earthquake happened in 2008. The experimental results demonstrate that SG-BiTLSTM network shows remarkable improvements on the recognition of landslide and hazard-affected bodies, compared with the traditional LSTM (the Baseline Model), the BLEU1 of the SG-BiTLSTM is improved by 5.89%, the matching rate between the mask maps and the focus matrix of the attention is improved by 42.81%. In conclusion, the SG-BiTLSTM network can recognize landslides and the hazard-affected bodies simultaneously to provide basic geographic information service for emergency decision-making.

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Section: Semantic Segmentationmentioning

confidence: 99%

Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM

Cui

Meng

et al. 2020

IJGI

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“…In this case, with fixed and limited receptive fields, it may cause more misclassifications because of the loss of multi-scale contextual information. To solve this problem, the hierarchical cascade structure proposed in a previous study (Fu et al, 2019c) was used. This structure generally enlarges the receptive field and increases the sampling rate by creating a hierarchical cascade structure using the atrous convolution layers (as shown in the central part of Fig.…”

Section: Hierarchical Cascade Structurementioning

confidence: 99%

“…However, since almost all these methods are proposed based on the handcrafted features, it is inherently difficult for them to achieve balance between high discriminability and good robustness (L. Zhang et al, 2016). To solve such problems, the remote sensing community has started to incorporate deep fully convolutional neural networks (FCN) within marine aquaculture detection tasks using high spatial resolution (HSR) images at local scales (Cui et al, 2019;Fu et al, 2019c;Shi et al, 2018). However, the opportunities associated with analyses of the high volumes of publicly available and free remote sensing data at medium resolution, such as Landsat, Sentinel-2 A/B, and GaoFen-1 wide-field-of-view (GF-1 WFV) imagery, have not been exploited.…”

mentioning

confidence: 99%

A new satellite-derived dataset for marine aquaculture areas in the China's coastal region

Fu¹,

Deng²,

Wang³

et al. 2020

Preprint

Self Cite

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Abstract. China has witnessed extensive development of the marine aquaculture industry over recent years. However, such rapid and disordered expansion posed risks to coastal environment, economic development, and biodiversity protection. This study aimed to produce an accurate national-scale marine aquaculture map at a spatial resolution of 16 m, using a proposed deep convolution neural networks (CNNs) based model and applied it to satellite data from China's GF-1 sensor in an end-to-end way. The analyses used homogeneous CNNs to extract high-dimensional features from the input imagery and preserve information at full resolution. Then, a hierarchical cascade architecture was followed to capture multi-scale features and contextual information. This hierarchical cascade homogeneous neural network (HCHNet) was found to achieve better classification performance than current state-of-the-art models (FCN-32s, Deeplab V2, U-Net, and HCNet). The resulting marine aquaculture area map has an overall classification accuracy > 95 % (95.2 %–96.4, 95 % confidence interval). And marine aquaculture was found to cover a total area of ~1100 km2 (1096.8 km2–1110.6 km2, 95 % confidence interval) in China, of which more than 85 % are marine plant culture areas, with 87 % found in the Fujian, Shandong, Liaoning, and Jiangsu provinces. The results confirm the applicability and effectiveness of HCHNet when applied to GF-1 data, identifying notable spatial distributions of different marine aquaculture areas and supporting the sustainable management and ecological assessments of coastal resources at a national scale. The national-scale marine aquaculture map at 16 m spatial resolution is published in the Google Maps kmz File Format with georeferencing information at https://doi.org/10.5281/zenodo.3881612 (Fu et al., 2020).

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“…In general, semantic segmentation of remote sensing images based on a CNN has been developed from a simple transplanting network structure to the design of a creative network structure [48] according to the characteristics of remote sensing and has achieved good results. Application scopes are expanded from building extracting [49], built-up area extracting [48], and mapping impervious surfaces [50] to oil palm tree detection [51].…”

Section: Cnn Seriesmentioning

confidence: 99%

Multi-Scale Semantic Segmentation and Spatial Relationship Recognition of Remote Sensing Images Based on an Attention Model

Cui

Wang

et al. 2019

Remote Sensing

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A comprehensive interpretation of remote sensing images involves not only remote sensing object recognition but also the recognition of spatial relations between objects. Especially in the case of different objects with the same spectrum, the spatial relationship can help interpret remote sensing objects more accurately. Compared with traditional remote sensing object recognition methods, deep learning has the advantages of high accuracy and strong generalizability regarding scene classification and semantic segmentation. However, it is difficult to simultaneously recognize remote sensing objects and their spatial relationship from end-to-end only relying on present deep learning networks. To address this problem, we propose a multi-scale remote sensing image interpretation network, called the MSRIN. The architecture of the MSRIN is a parallel deep neural network based on a fully convolutional network (FCN), a U-Net, and a long short-term memory network (LSTM). The MSRIN recognizes remote sensing objects and their spatial relationship through three processes. First, the MSRIN defines a multi-scale remote sensing image caption strategy and simultaneously segments the same image using the FCN and U-Net on different spatial scales so that a two-scale hierarchy is formed. The output of the FCN and U-Net are masked to obtain the location and boundaries of remote sensing objects. Second, using an attention-based LSTM, the remote sensing image captions include the remote sensing objects (nouns) and their spatial relationships described with natural language. Finally, we designed a remote sensing object recognition and correction mechanism to build the relationship between nouns in captions and object mask graphs using an attention weight matrix to transfer the spatial relationship from captions to objects mask graphs. In other words, the MSRIN simultaneously realizes the semantic segmentation of the remote sensing objects and their spatial relationship identification end-to-end. Experimental results demonstrated that the matching rate between samples and the mask graph increased by 67.37 percentage points, and the matching rate between nouns and the mask graph increased by 41.78 percentage points compared to before correction. The proposed MSRIN has achieved remarkable results. the existing studies do not address the interpretation of spatial relationships between remote sensing objects, which limits the understanding of remote sensing objects, especially when the phenomenon of different objects with the same spectrum in remote sensing appears.The phenomenon of different objects with the same spectrum in remote sensing is quite common. It is difficult to identify objects only by their own textures, spectra, and shape information. Object identification requires multi-scale semantic information and spatially adjacent objects to assist in decision-making. The spatial relationship between remote sensing objects is of great significance to the recognition of remote sensing objects when different objects have the same spectru...

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Mapping Impervious Surfaces in Town–Rural Transition Belts Using China’s GF-2 Imagery and Object-Based Deep CNNs

Cited by 34 publications

References 50 publications

Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM

Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM

A new satellite-derived dataset for marine aquaculture areas in the China's coastal region

Multi-Scale Semantic Segmentation and Spatial Relationship Recognition of Remote Sensing Images Based on an Attention Model

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