Target detection from high-resolution remote sensing images using deep learning methods

Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. Different proxy variables indicating species richness and quality of the sites are essential for efficient detecting and monitoring forest biodiversity. European aspen (Populus tremula L.) is a minor deciduous tree species with a high importance in maintaining biodiversity in boreal forests. Large aspen trees host hundreds of species, many of them classified as threatened. However, accurate fine-scale spatial data on aspen occurrence remains scarce and incomprehensive.&#160;We studied detection of aspen using different remote sensing techniques in Evo, southern Finland. Our study area of 83 km2 contains both managed and protected southern boreal forests characterized by Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), whereas European aspen has a relatively sparse and scattered occurrence in the area. We collected high-resolution airborne hyperspectral and airborne laser scanning data covering the whole study area and ultra-high resolution unmanned aerial vehicle (UAV) data with RGB and multispectral sensors from selected parts of the area. We tested the discrimination of aspen from other species at tree level using different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D convolutional neural networks).&#160;Airborne hyperspectral and lidar data gave excellent results with machine learning and deep learning classification methods The highest classification accuracies for aspen varied between 91-92% (F1-score). The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724&#8211;727 nm) and shortwave infrared (1520&#8211;1564 nm and 1684&#8211;1706 nm) (Viinikka et al. 2020; M&#228;yr&#228; et al 2021). Aspen detection using RGB and multispectral data also gave good results (highest F1-score of aspen = 87%) (Kuzmin et al 2021). Different remote sensing data enabled production of a spatially explicit map of aspen occurrence in the study area. Information on aspen occurrence and abundance can significantly contribute to biodiversity management and conservation efforts in boreal forests. Our results can be further utilized in upscaling efforts aiming at aspen detection over larger geographical areas using satellite images.

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Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Remote Sensing ◽

10.3390/rs12182985 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2985 ◽

Cited By ~ 1

Author(s):

Yeneng Lin ◽

Dongyun Xu ◽

Nan Wang ◽

Zhou Shi ◽

Qiuxiao Chen

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Network Models ◽

Road Extraction ◽

Remote Sensing Images ◽

Proposed Model ◽

Wide Range ◽

Network Modules ◽

Very High

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

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Geospatial Target Detection from High-Resolution Remote-Sensing Images Based on PIIFD Descriptor and Salient Regions

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-019-00944-4 ◽

2019 ◽

Vol 47 (5) ◽

pp. 879-891

Author(s):

Fariborz Ghorbani ◽

Hamid Ebadi ◽

Amin Sedaghat

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Target Detection ◽

Remote Sensing Images ◽

Salient Regions

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Vehicle Detection in High Resolution Satellite Remote Sensing Images Based on Deep Learning

IEEE Access ◽

10.1109/access.2020.3017894 ◽

2020 ◽

Vol 8 ◽

pp. 153394-153402

Author(s):

Qulin Tan ◽

Juan Ling ◽

Jun Hu ◽

Xiaochun Qin ◽

Jiping Hu

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Satellite Remote Sensing ◽

Vehicle Detection ◽

Remote Sensing Images

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A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images

Remote Sensing Letters ◽

10.1080/2150704x.2018.1557791 ◽

2019 ◽

Vol 10 (4) ◽

pp. 381-390 ◽

Cited By ~ 12

Author(s):

Ye Li ◽

Lele Xu ◽

Jun Rao ◽

Lili Guo ◽

Zhen Yan ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Learning Method ◽

Remote Sensing Images ◽

Road Segmentation

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A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs12223845 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3845

Author(s):

Zhiyu Xu ◽

Yi Zhou ◽

Shixin Wang ◽

Litao Wang ◽

Feng Li ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Classification Accuracy ◽

Green Space ◽

Urban Green Space ◽

Classification Method ◽

Deciduous Trees ◽

Remote Sensing Images ◽

Urban Green

The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.

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