scholarly journals MCANet: A joint semantic segmentation framework of optical and SAR images for land use classification

2022 ◽  
Vol 106 ◽  
pp. 102638
Author(s):  
Xue Li ◽  
Guo Zhang ◽  
Hao Cui ◽  
Shasha Hou ◽  
Shunyao Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Semantic Segmentation ◽  
Land Use Classification ◽  
Sar Images ◽  
Segmentation Framework

scholarly journals EXPLORING SEMANTIC RELATIONSHIPS FOR HIERARCHICAL LAND USE CLASSIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORKS

2020 ◽  
Vol V-2-2020 ◽  
pp. 599-607
Author(s):  
C. Yang ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Land Use ◽  
Information Source ◽  
Semantic Category ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Land Use Classification ◽  
Semantic Relationships ◽  
Fine Grained ◽  
Semantic Dependencies ◽  
Number Of Classes

Abstract. Land use (LU) is an important information source commonly stored in geospatial databases. Most current work on automatic LU classification for updating topographic databases considers only one category level (e.g. residential or agricultural) consisting of a small number of classes. However, LU databases frequently contain very detailed information, using a hierarchical object catalogue where the number of categories differs depending on the hierarchy level. This paper presents a method for the classification of LU on the basis of aerial images that differentiates a fine-grained class structure, exploiting the hierarchical relationship between categories at different levels of the class catalogue. Starting from a convolutional neural network (CNN) for classifying the categories of all levels, we propose a strategy to simultaneously learn the semantic dependencies between different category levels explicitly. The input to the CNN consists of aerial images and derived data as well as land cover information derived from semantic segmentation. Its output is the class scores at three different semantic levels, based on which predictions that are consistent with the class hierarchy are made. We evaluate our method using two test sites and show how the classification accuracy depends on the semantic category level. While at the coarsest level, an overall accuracy in the order of 90% can be achieved, at the finest level, this accuracy is reduced to around 65%. Our experiments also show which classes are particularly hard to differentiate.

scholarly journals Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery

2021 ◽  
Vol 13 (12) ◽  
pp. 2292
Author(s):  
Oscar D. Pedrayes ◽  
Darío G. Lema ◽  
Daniel F. García ◽  
Rubén Usamentiaga ◽  
Ángela Alonso
Keyword(s):  
Land Use ◽  
Satellite Imagery ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Aerial Imagery ◽  
Land Use Classification ◽  
Cost Performance ◽  
Sampling Distance ◽  
Segmentation Methods ◽  
Sentinel 2

Land use classification using aerial imagery can be complex. Characteristics such as ground sampling distance, resolution, number of bands and the information these bands convey are the keys to its accuracy. Random Forest is the most widely used approach but better and more modern alternatives do exist. In this paper, state-of-the-art methods are evaluated, consisting of semantic segmentation networks such as UNet and DeepLabV3+. In addition, two datasets based on aircraft and satellite imagery are generated as a new state of the art to test land use classification. These datasets, called UOPNOA and UOS2, are publicly available. In this work, the performance of these networks and the two datasets generated are evaluated. This paper demonstrates that ground sampling distance is the most important factor in obtaining good semantic segmentation results, but a suitable number of bands can be as important. This proves that both aircraft and satellite imagery can produce good results, although for different reasons. Finally, cost performance for an inference prototype is evaluated, comparing various Microsoft Azure architectures. The evaluation concludes that using a GPU is unnecessarily costly for deployment. A GPU need only be used for training.

scholarly journals Land Use Classification of the Deep Convolutional Neural Network Method Reducing the Loss of Spatial Features

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2792 ◽  
Author(s):  
Xuedong Yao ◽  
Hui Yang ◽  
Yanlan Wu ◽  
Penghai Wu ◽  
Biao Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Land Use ◽  
Convolutional Neural Networks ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Land Use Classification ◽  
Deep Convolutional Neural Networks ◽  
Remote Sensing Imagery ◽  
Spatial Features

Land use classification is a fundamental task of information extraction from remote sensing imagery. Semantic segmentation based on deep convolutional neural networks (DCNNs) has shown outstanding performance in this task. However, these methods are still affected by the loss of spatial features. In this study, we proposed a new network, called the dense-coordconv network (DCCN), to reduce the loss of spatial features and strengthen object boundaries. In this network, the coordconv module is introduced into the improved DenseNet architecture to improve spatial information by putting coordinate information into feature maps. The proposed DCCN achieved an obvious performance in terms of the public ISPRS (International Society for Photogrammetry and Remote Sensing) 2D semantic labeling benchmark dataset. Compared with the results of other deep convolutional neural networks (U-net, SegNet, Deeplab-V3), the results of the DCCN method improved a lot and the OA (overall accuracy) and mean F1 score reached 89.48% and 86.89%, respectively. This indicates that the DCCN method can effectively reduce the loss of spatial features and improve the accuracy of semantic segmentation in high resolution remote sensing imagery.

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