Semantic segmentation of very high-resolution remote sensing image based on multiple band combinations and patchwise scene analysis

2020 ◽  
Vol 14 (01) ◽  
pp. 1 ◽  
Author(s):  
Zhen Zhang ◽  
Jue Huang ◽  
Tao Jiang ◽  
Baikai Sui ◽  
Xinliang Pan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Scene Analysis ◽  
Multiple Band ◽  
Band Combinations ◽  
Very High

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

scholarly journals U-net Network for Building Information Extraction of Remote-Sensing Imagery

2018 ◽  
Vol 14 (12) ◽  
pp. 179
Author(s):  
Jingtan Li ◽  
Maolin Xu ◽  
Hongling Xiu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Extraction ◽  
Image Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Building Information ◽  
The Face

With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.

scholarly journals A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 482 ◽  
Author(s):  
Qi Bi ◽  
Kun Qin ◽  
Han Zhang ◽  
Ye Zhang ◽  
Zhili Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Common Knowledge ◽  
Remote Sensing Image ◽  
Morphological Operations ◽  
Building Extraction ◽  
Multi Scale ◽  
Training Samples ◽  
Image Building ◽  
Very High

Building extraction plays a significant role in many high-resolution remote sensing image applications. Many current building extraction methods need training samples while it is common knowledge that different samples often lead to different generalization ability. Morphological building index (MBI), representing morphological features of building regions in an index form, can effectively extract building regions especially in Chinese urban regions without any training samples and has drawn much attention. However, some problems like the heavy computation cost of multi-scale and multi-direction morphological operations still exist. In this paper, a multi-scale filtering building index (MFBI) is proposed in the hope of overcoming these drawbacks and dealing with the increasing noise in very high-resolution remote sensing image. The profile of multi-scale average filtering is averaged and normalized to generate this index. Moreover, to fully utilize the relatively little spectral information in very high-resolution remote sensing image, two scenarios to generate the multi-channel multi-scale filtering index (MMFBI) are proposed. While no high-resolution remote sensing image building extraction dataset is open to the public now and the current very high-resolution remote sensing image building extraction datasets usually contain samples from the Northern American or European regions, we offer a very high-resolution remote sensing image building extraction datasets in which the samples contain multiple building styles from multiple Chinese regions. The proposed MFBI and MMFBI outperform MBI and the currently used object based segmentation method on the dataset, with a high recall and F-score. Meanwhile, the computation time of MFBI and MBI is compared on three large-scale very high-resolution satellite image and the sensitivity analysis demonstrates the robustness of the proposed method.

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 Fully Convolutional Neural Network with Augmented Atrous Spatial Pyramid Pool and Fully Connected Fusion Path for High Resolution Remote Sensing Image Segmentation

2019 ◽  
Vol 9 (9) ◽  
pp. 1816 ◽  
Author(s):  
Guangsheng Chen ◽  
Chao Li ◽  
Wei Wei ◽  
Weipeng Jing ◽  
Marcin Woźniak ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Dilated Convolution ◽  
Segmentation Task ◽  
Fully Connected ◽  
Spatial Pyramid

Recent developments in Convolutional Neural Networks (CNNs) have allowed for the achievement of solid advances in semantic segmentation of high-resolution remote sensing (HRRS) images. Nevertheless, the problems of poor classification of small objects and unclear boundaries caused by the characteristics of the HRRS image data have not been fully considered by previous works. To tackle these challenging problems, we propose an improved semantic segmentation neural network, which adopts dilated convolution, a fully connected (FC) fusion path and pre-trained encoder for the semantic segmentation task of HRRS imagery. The network is built with the computationally-efficient DeepLabv3 architecture, with added Augmented Atrous Spatial Pyramid Pool and FC Fusion Path layers. Dilated convolution enlarges the receptive field of feature points without decreasing the feature map resolution. The improved neural network architecture enhances HRRS image segmentation, reaching the classification accuracy of 91%, and the precision of recognition of small objects is improved. The applicability of the improved model to the remote sensing image segmentation task is verified.

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