scholarly journals SEMANTIC SEGMENTATION OF CONVOLUTIONAL NEURAL NETWORK FOR SUPERVISED CLASSIFICATION OF MULTISPECTRAL REMOTE SENSING

2018 ◽  
Vol XLII-3 ◽  
pp. 2035-2039
Author(s):  
L. Xue ◽  
C. Liu ◽  
Y. Wu ◽  
H. Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Remote Sensing Imagery ◽  
The Neural Network ◽  
Ground Object ◽  
Remote Sensing Image Processing

Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remote Sensing Imagery is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for ground object segmentation and the results could be further improved. This paper used convolution neural network named U-Net, its structure has a contracting path and an expansive path to get high resolution output. In the network , We added BN layers, which is more conducive to the reverse pass. Moreover, after upsampling convolution , we add dropout layers to prevent overfitting. They are promoted to get more precise segmentation results. To verify this network architecture, we used a Kaggle dataset. Experimental results show that U-Net achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

Classification of Individual Tree Species in High-Resolution Remote Sensing Imagery Based on Convolution Neural Network

2021 ◽  
Vol 58 (2) ◽  
pp. 0228002
Author(s):  
欧阳光 Ouyang Guang ◽  
荆林海 Jing Linhai ◽  
阎世杰 Yan Shijie ◽  
李慧 Li Hui ◽  
唐韵玮 Tang Yunwei ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Tree Species ◽  
Convolution Neural Network ◽  
Individual Tree ◽  
Remote Sensing Imagery

scholarly journals AN ADAPTIVE MORPHOLOGICAL MEAN FILTER FOR VERY HIGH-RESOLUTION REMOTE SENSING IMAGE PROCESSING

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1269-1274
Author(s):  
Z. Lv ◽  
J. Shi ◽  
Y. Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Median Filter ◽  
Remote Sensing Image ◽  
Mean Filter ◽  
Ground Object ◽  
The Mean ◽  
Remote Sensing Image Processing ◽  
Very High

Very high resolution (VHR) remote sensing imagery can reveal the ground object in greater detail, depicting their color, shape, size and structure. However, VHR also leads much original noise in spectra, and this original noise may reduce the reliability of the classification’s result. This paper presents an Adaptive Morphological Mean Filter (AMMF) for smoothing the original noise of VHR imagery and improving the classification’s performance. AMMF is a shape-adaptive filter which is constructed by detecting gradually the spectral similarity between a kernel-anchored pixel and its contextual pixels through an extension-detector with 8-neighbouring pixels, and the spectral value of the kernel-anchored pixel is instead by the mean of group pixels within the adaptive region. The classification maps based on the AMMF are compared with the classification of VHR images based on the homologous filter processing, such as Mean Filter (MF) and Median Filter(MedF). The experimental results suggest the following: 1) VHR image processed using AMMF can not only preserve the detail information among inter-classes but also smooth the noise within intra-class; 2) The proposed AMMF processing can improve the classification’s performance of VHR image, and it obtains a better visual performance and accuracy while comparing with MF and MedF.

scholarly journals NAS-HRIS: Automatic Design and Architecture Search of Neural Network for Semantic Segmentation in Remote Sensing Images

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5292 ◽  
Author(s):  
Mingwei Zhang ◽  
Weipeng Jing ◽  
Jingbo Lin ◽  
Nengzhen Fang ◽  
Wei Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Network Architecture ◽  
Modern Society ◽  
Semantic Segmentation ◽  
Search Space ◽  
Remote Sensing Images ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Urban Modelling

The segmentation of high-resolution (HR) remote sensing images is very important in modern society, especially in the fields of industry, agriculture and urban modelling. Through the neural network, the machine can effectively and accurately extract the surface feature information. However, using the traditional deep learning methods requires plentiful efforts in order to find a robust architecture. In this paper, we introduce a neural network architecture search (NAS) method, called NAS-HRIS, which can automatically search neural network architecture on the dataset. The proposed method embeds a directed acyclic graph (DAG) into the search space and designs the differentiable searching process, which enables it to learn an end-to-end searching rule by using gradient descent optimization. It uses the Gumbel-Max trick to provide an efficient way when drawing samples from a non-continuous probability distribution, and it improves the efficiency of searching and reduces the memory consumption. Compared with other NAS, NAS-HRIS consumes less GPU memory without reducing the accuracy, which corresponds to a large amount of HR remote sensing imagery data. We have carried out experiments on the WHUBuilding dataset and achieved 90.44% MIoU. In order to fully demonstrate the feasibility of the method, we made a new urban Beijing Building dataset, and conducted experiments on satellite images and non-single source images, achieving better results than SegNet, U-Net and Deeplab v3+ models, while the computational complexity of our network architecture is much smaller.

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