scholarly journals From Statistical Detection to Decision Fusion: Detection of Underwater Mines in High Resolution SAS Images

10.5772/39411 ◽  
2009 ◽  
Author(s):  
Frdric Maussang ◽  
Jocelyn Chanussot ◽  
Michle Rombaut ◽  
Maud Amate
Keyword(s):  
High Resolution ◽  
Decision Fusion ◽  
Statistical Detection ◽  
Fusion Detection

scholarly journals Research on Cultivated Land Change Based on GIS and High Resolution Remote Sensing Images

2018 ◽  
Vol 228 ◽  
pp. 02013
Author(s):  
Haibo Yu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Automatic Monitoring ◽  
Research Area ◽  
Decision Fusion ◽  
Land Change ◽  
Fuzzy Decision ◽  
Remote Sensing Images ◽  
Monitoring Method ◽  
Gis Data

This paper study an automatic monitoring method for land change based on high resolution remote sensing images and GIS data, and we use three classification methods to classify and fuse the research area. Secondly, the paper calculates the corresponding map class components and compares them with their historical attributes; it can automatically monitor land use change. The experimental results show that the fuzzy decision fusion classification can significantly improve the classification effect, and it can accurately determine the change area accurately and automatically. However, there are some partial errors in the region.

scholarly journals Automatic Building Detection from High-Resolution Remote Sensing Images Based on Joint Optimization and Decision Fusion of Morphological Attribute Profiles

2021 ◽  
Vol 13 (3) ◽  
pp. 357 ◽  
Author(s):  
Chao Wang ◽  
Yan Zhang ◽  
Xiaohui Chen ◽  
Hao Jiang ◽  
Mithun Mukherjee ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Decision Fusion ◽  
Joint Optimization ◽  
Selection Strategy ◽  
Building Detection ◽  
Adaptive Selection ◽  
Processing Step ◽  
Morphological Attribute ◽  
Selection Of

High-resolution remote sensing (HRRS) images, when used for building detection, play a key role in urban planning and other fields. Compared with the deep learning methods, the method based on morphological attribute profiles (MAPs) exhibits good performance in the absence of massive annotated samples. MAPs have been proven to have a strong ability for extracting detailed characterizations of buildings with multiple attributes and scales. So far, a great deal of attention has been paid to this application. Nevertheless, the constraints of rational selection of attribute scales and evidence conflicts between attributes should be overcome, so as to establish reliable unsupervised detection models. To this end, this research proposes a joint optimization and fusion building detection method for MAPs. In the pre-processing step, the set of candidate building objects are extracted by image segmentation and a set of discriminant rules. Second, the differential profiles of MAPs are screened by using a genetic algorithm and a cross-probability adaptive selection strategy is proposed; on this basis, an unsupervised decision fusion framework is established by constructing a novel statistics-space building index (SSBI). Finally, the automated detection of buildings is realized. We show that the proposed method is significantly better than the state-of-the-art methods on HRRS images with different groups of different regions and different sensors, and overall accuracy (OA) of our proposed method is more than 91.9%.

Deep Learning Enhances the Detection of Breeding Birds in UAV Images

2021 ◽  
Author(s):  
Benjamin Kellenberger ◽  
Thor Veen ◽  
Eelke Folmer ◽  
Devis Tuia
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Large Scale ◽  
Bird Species ◽  
Decision Fusion ◽  
Training Data ◽  
Seabird Species ◽  
Uav Images

<p>Recently, Unmanned Aerial Vehicles (UAVs) equipped with high-resolution imaging sensors have become a viable alternative for ecologists to conduct wildlife censuses, compared to foot surveys. They cause less disturbance by sensing remotely, they provide coverage of otherwise inaccessible areas, and their images can be reviewed and double-checked in controlled screening sessions. However, the amount of data they generate often makes this photo-interpretation stage prohibitively time-consuming.</p><p>In this work, we automate the detection process with deep learning [4]. We focus on counting coastal seabirds on sand islands off the West African coast, where species like the African Royal Tern are at the top of the food chain [5]. Monitoring their abundance provides invaluable insights into biodiversity in this area [7]. In a first step, we obtained orthomosaics from nadir-looking UAVs over six sand islands with 1cm resolution. We then fully labelled one of them with points for four seabird species, which required three weeks for five annotators to do and resulted in over 21,000 individuals. Next, we further labelled the other five orthomosaics, but in an incomplete manner; we aimed for a low number of only 200 points per species. These points, together with a few background polygons, served as training data for our ResNet-based [2] detection model. This low number of points required multiple strategies to obtain stable predictions, including curriculum learning [1] and post-processing by a Markov random field [6]. In the end, our model was able to accurately predict the 21,000 birds of the test image with 90% precision at 90% recall (Fig. 1) [3]. Furthermore, this model required a mere 4.5 hours from creating training data to the final prediction, which is a fraction of the three weeks needed for the manual labelling process. Inference time is only a few minutes, which makes the model scale favourably to many more islands. In sum, the combination of UAVs and machine learning-based detectors simultaneously provides census possibilities with unprecedentedly high accuracy and comparably minuscule execution time.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.bc5211f4f60067568601161/sdaolpUECMynit/12UGE&app=m&a=0&c=eeda7238e992b9591c2fec19197f67dc&ct=x&pn=gnp.elif&d=1" alt=""></p><p><em>Fig. 1: Our model is able to predict over 21,000 birds in high-resolution UAV images in a fraction of time compared to weeks of manual labelling.</em></p><p> </p><p>References</p><p>1. Bengio, Yoshua, et al. "Curriculum learning." Proceedings of the 26th annual international conference on machine learning. 2009.</p><p>2. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.</p><p>3. Kellenberger, Benjamin, et al. “21,000 Birds in 4.5 Hours: Efficient Large-scale Seabird Detection with Machine Learning.” Remote Sensing in Ecology and Conservation. Under review.</p><p>4. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444.</p><p>5. Parsons, Matt, et al. "Seabirds as indicators of the marine environment." ICES Journal of Marine Science 65.8 (2008): 1520-1526.</p><p>6. Tuia, Devis, Michele Volpi, and Gabriele Moser. "Decision fusion with multiple spatial supports by conditional random fields." IEEE Transactions on Geoscience and Remote Sensing 56.6 (2018): 3277-3289.</p><p>7. Veen, Jan, Hanneke Dallmeijer, and Thor Veen. "Selecting piscivorous bird species for monitoring environmental change in the Banc d'Arguin, Mauritania." Ardea 106.1 (2018): 5-18.</p>

scholarly journals High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Chao Wang ◽  
Hui Liu ◽  
Yi Shen ◽  
Kaiguang Zhao ◽  
Hongyan Xing ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Land Surface ◽  
Spatial Information ◽  
Decision Fusion ◽  
Information Modeling ◽  
Observation Data ◽  
Scale Parameters ◽  
Morphological Attribute

Change detection (CD) is essential for accurate understanding of land surface changes with multitemporal Earth observation data. Due to the great advantages in spatial information modeling, Morphological Attribute Profiles (MAPs) are becoming increasingly popular for improving the recognition ability in CD applications. However, most of the MAPs-based CD methods are implemented by setting the scale parameters of Attribute Profiles (APs) manually and ignoring the uncertainty of change information from different sources. To address these issues, a novel method for CD in high-resolution remote sensing (HRRS) images based on morphological attribute profiles and decision fusion is proposed in this study. By establishing the objective function based on the minimum of average interscale correlation, a morphological attribute profile with adaptive scale parameters (ASP-MAPs) is presented to exploit the spatial structure information. On this basis, a multifeature decision fusion framework based on the Dempster–Shafer (D-S) theory is constructed for obtaining the CD map. Experiments of multitemporal HRRS images from different sensors have shown that the proposed method outperforms the other advanced comparison CD methods, and the overall accuracy (OA) can reach more than 83.9%.

scholarly journals Fusion of Multiscale Convolutional Neural Networks for Building Extraction in Very High-Resolution Images

2019 ◽  
Vol 11 (3) ◽  
pp. 227 ◽  
Author(s):  
Genyun Sun ◽  
Hui Huang ◽  
Aizhu Zhang ◽  
Feng Li ◽  
Huimin Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Decision Fusion ◽  
Superior Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Fusion Strategy ◽  
Very High

Extracting buildings from very high resolution (VHR) images has attracted much attention but is still challenging due to their large varieties in appearance and scale. Convolutional neural networks (CNNs) have shown effective and superior performance in automatically learning high-level and discriminative features in extracting buildings. However, the fixed receptive fields make conventional CNNs insufficient to tolerate large scale changes. Multiscale CNN (MCNN) is a promising structure to meet this challenge. Unfortunately, the multiscale features extracted by MCNN are always stacked and fed into one classifier, which make it difficult to recognize objects with different scales. Besides, the repeated sub-sampling processes lead to a blurred boundary of the extracted features. In this study, we proposed a novel parallel support vector mechanism (SVM)-based fusion strategy to take full use of deep features at different scales as extracted by the MCNN structure. We firstly designed a MCNN structure with different sizes of input patches and kernels, to learn multiscale deep features. After that, features at different scales were individually fed into different support vector machine (SVM) classifiers to produce rule images for pre-classification. A decision fusion strategy is then applied on the pre-classification results based on another SVM classifier. Finally, superpixels are applied to refine the boundary of the fused results using region-based maximum voting. For performance evaluation, the well-known International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam dataset was used in comparison with several state-of-the-art algorithms. Experimental results have demonstrated the superior performance of the proposed methodology in extracting complex buildings in urban districts.

Observations of the spatial structure of interstellar hydrogen

1967 ◽  
Vol 31 ◽  
pp. 45-46
Author(s):  
Carl Heiles
Keyword(s):  
High Resolution ◽  
Spatial Structure ◽  
Velocity Dispersion ◽  
Temperature Variations

High-resolution 21-cm line observations in a region aroundlII= 120°,b11= +15°, have revealed four types of structure in the interstellar hydrogen: a smooth background, large sheets of density 2 atoms cm-3, clouds occurring mostly in groups, and ‘Cloudlets’ of a few solar masses and a few parsecs in size; the velocity dispersion in the Cloudlets is only 1 km/sec. Strong temperature variations in the gas are in evidence.

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