Object-based city land cover classification and change analysis with multi-temporal high resolution remote sensing images in Jiangyin

2013 ◽  
Author(s):  
Ning Xiaogang ◽  
Zhang Jixian ◽  
Chen Zhiyong
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Remote Sensing Images ◽  
Change Analysis ◽  
Object Based ◽  
Multi Temporal

scholarly journals Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using CNN-Based Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 189 ◽  
Author(s):  
Chi Zhang ◽  
Shiqing Wei ◽  
Shunping Ji ◽  
Meng Lu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Classification ◽  
Remote Sensing Images ◽  
Urban Land Cover ◽  
Object Based ◽  
Very High ◽  
Change Map

The study investigates land use/cover classification and change detection of urban areas from very high resolution (VHR) remote sensing images using deep learning-based methods. Firstly, we introduce a fully Atrous convolutional neural network (FACNN) to learn the land cover classification. In the FACNN an encoder, consisting of full Atrous convolution layers, is proposed for extracting scale robust features from VHR images. Then, a pixel-based change map is produced based on the classification map of current images and an outdated land cover geographical information system (GIS) map. Both polygon-based and object-based change detection accuracy is investigated, where a polygon is the unit of the GIS map and an object consists of those adjacent changed pixels on the pixel-based change map. The test data covers a rapidly developing city of Wuhan (8000 km2), China, consisting of 0.5 m ground resolution aerial images acquired in 2014, and 1 m ground resolution Beijing-2 satellite images in 2017, and their land cover GIS maps. Testing results showed that our FACNN greatly exceeded several recent convolutional neural networks in land cover classification. Second, the object-based change detection could achieve much better results than a pixel-based method, and provide accurate change maps to facilitate manual urban land cover updating.

scholarly journals Coastal Land Cover Classification of High-Resolution Remote Sensing Images Using Attention-Driven Context Encoding Network

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7032
Author(s):  
Jifa Chen ◽  
Gang Chen ◽  
Lizhe Wang ◽  
Bo Fang ◽  
Ping Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Long Range ◽  
Land Cover Classification ◽  
Local Context ◽  
Remote Sensing Images ◽  
Evaluation Indexes ◽  
Additional Information ◽  
Coastal Land

Low inter-class variance and complex spatial details exist in ground objects of the coastal zone, which leads to a challenging task for coastal land cover classification (CLCC) from high-resolution remote sensing images. Recently, fully convolutional neural networks have been widely used in CLCC. However, the inherent structure of the convolutional operator limits the receptive field, resulting in capturing the local context. Additionally, complex decoders bring additional information redundancy and computational burden. Therefore, this paper proposes a novel attention-driven context encoding network to solve these problems. Among them, lightweight global feature attention modules are employed to aggregate multi-scale spatial details in the decoding stage. Meanwhile, position and channel attention modules with long-range dependencies are embedded to enhance feature representations of specific categories by capturing the multi-dimensional global context. Additionally, multiple objective functions are introduced to supervise and optimize feature information at specific scales. We apply the proposed method in CLCC tasks of two study areas and compare it with other state-of-the-art approaches. Experimental results indicate that the proposed method achieves the optimal performances in encoding long-range context and recognizing spatial details and obtains the optimum representations in evaluation indexes.

scholarly journals Land-cover classification with high-resolution remote sensing images using transferable deep models

2020 ◽  
Vol 237 ◽  
pp. 111322 ◽  
Author(s):  
Xin-Yi Tong ◽  
Gui-Song Xia ◽  
Qikai Lu ◽  
Huanfeng Shen ◽  
Shengyang Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Remote Sensing Images
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