scholarly journals Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models

2021 ◽  
pp. 1-1
Author(s):  
Sanja Scepanovic ◽  
Oleg Antropov ◽  
Pekka Laurila ◽  
Yrjo Akseli Rauste ◽  
Vladimir Ignatenko ◽  
...  
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Semantic Segmentation ◽  
Wide Area ◽  
Land Cover Mapping ◽  
Segmentation Models

Large-Scale Land Cover Mapping on Sentinel-1 SAR Imagery Using Deep Transfer Learning

2019 ◽  
Author(s):  
Sanja Šćepanović ◽  
Oleg Antropov ◽  
Pekka Laurila ◽  
Vladimir Ignatenko ◽  
Jaan Praks
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Land Cover ◽  
Transfer Learning ◽  
Large Scale ◽  
Geographical Area ◽  
Wide Area ◽  
Land Cover Mapping ◽  
Learning Models ◽  
Sar Imagery

Land cover mapping and monitoring are essential for understanding the environment and the effects of human activities on the environment. The automatic approaches to land cover mapping are predominantly based on the traditional machine learning that requires heuristic feature design. Such approaches are relatively slow and they are often suitable only for a particular type of satellite sensor or geographical area. Recently, deep learning has outperformed traditional machine learning approaches on a range of image processing tasks including image classification and segmentation. In this study, we demonstrated the suitability of deep learning models to land cover mapping on a large scale using satellite C-band SAR images. We used a set of 14 ESA Sentinel-1 scenes acquired during the summer season over a wide area in Finland representative of the land cover in the country. These imagery were used as an input to seven state-of-the-art deep-learning models for semantic segmentation, namely U-Net, DeepLabV3+, PSPNet, BiSeNet, SegNet, FC-DenseNet, and FRRN-B. These models were pre-trained on the ImageNet dataset and further fine-tuned in this study. To the best of our knowledge, this is the first successful demonstration of transfer learning for SAR imagery in the context of wide-area land-cover mapping. CORINE land cover map produced by the Finnish Environment Institute was used as a reference, and the models were trained to distinguish between 5 Level-1 CORINE classes. Upon the evaluation and benchmarking, we found that all the models demonstrated solid performance, with the top FC-DenseNet model achieving an overall accuracy of 90.66%. These results indicate the suitability of deep learning methods to support efficient wide-area mapping using satellite SAR imagery.

scholarly journals Super-resolution land cover mapping by deep learning

2019 ◽  
Vol 10 (6) ◽  
pp. 598-606 ◽  
Author(s):  
Feng Ling ◽  
Giles M. Foody
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Super Resolution ◽  
Land Cover Mapping

scholarly journals CAN SPOT-6/7 CNN SEMANTIC SEGMENTATION IMPROVE SENTINEL-2 BASED LAND COVER PRODUCTS? SENSOR ASSESSMENT AND FUSION

2020 ◽  
Vol V-2-2020 ◽  
pp. 557-564
Author(s):  
O. Stocker ◽  
A. Le Bris
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Data Fusion ◽  
Semantic Segmentation ◽  
Fine Grained ◽  
Satellite Sensors ◽  
Fusion Framework ◽  
Sentinel 2

Abstract. Needs for fine-grained, accurate and up-to-date land cover (LC) data are important to answer both societal and scientific purposes. Several automatic products have already been proposed, but are mostly generated out of satellite sensors like Sentinel-2 (S2) or Landsat. Metric sensors, e.g. SPOT-6/7, have been less considered, while they enable (at least annual) acquisitions at country scale and can now be efficiently processed thanks to deep learning (DL) approaches. This study thus aimed at assessing whether such sensor can improve such land cover products. A custom simple yet effective U-net - Deconv-Net inspired DL architecture is developed and applied to SPOT-6/7 and S2 for different LC nomenclatures, aiming at comparing the relevance of their spatial/spectral configurations and investigating their complementarity. The proposed DL architecture is then extended to data fusion and applied to previous sensors. At the end, the proposed fusion framework is used to enrich an existing S2 based LC product, as it is generic enough to cope with fusion at distinct levels.

scholarly journals Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model

2018 ◽  
Vol 10 (11) ◽  
pp. 1713 ◽  
Author(s):  
Wenzhi Zhao ◽  
William Emery ◽  
Yanchen Bo ◽  
Jiage Chen
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Spatial Information ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Learning Algorithms ◽  
Classification Performance ◽  
Higher Order ◽  
Land Cover Mapping ◽  
Initial Classification

Deep learning has become a standard processing procedure in land cover mapping for remote sensing images. Instead of relying on hand-crafted features, deep learning algorithms, such as Convolutional Neural Networks (CNN) can automatically generate effective feature representations, in order to recognize objects with complex image patterns. However, the rich spatial information still remains unexploited, since most of the deep learning algorithms only focus on small image patches that overlook the contextual information at larger scales. To utilize these contextual information and improve the classification performance for high-resolution imagery, we propose a graph-based model in order to capture the contextual information over semantic segments of the image. First, we explore semantic segments which build on the top of deep features and obtain the initial classification result. Then, we further improve the initial classification results with a higher-order co-occurrence model by extending the existing conditional random field (HCO-CRF) algorithm. Compared to the pixel- and object-based CNN methods, the proposed model achieved better performance in terms of classification accuracy.

scholarly journals Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2077 ◽  
Author(s):  
Shih-Yu Chen ◽  
Chinsu Lin ◽  
Guan-Jie Li ◽  
Yu-Chun Hsu ◽  
Keng-Hao Liu
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Life Cycle ◽  
Climate Changes ◽  
Imbalanced Data ◽  
Semantic Segmentation ◽  
Effect Of Temperature ◽  
Tree Leaves ◽  
The Difference ◽  
Segmentation Models

The life cycle of leaves, from sprout to senescence, is the phenomenon of regular changes such as budding, branching, leaf spreading, flowering, fruiting, leaf fall, and dormancy due to seasonal climate changes. It is the effect of temperature and moisture in the life cycle on physiological changes, so the detection of newly grown leaves (NGL) is helpful for the estimation of tree growth and even climate change. This study focused on the detection of NGL based on deep learning convolutional neural network (CNN) models with sparse enhancement (SE). As the NGL areas found in forest images have similar sparse characteristics, we used a sparse image to enhance the signal of the NGL. The difference between the NGL and the background could be further improved. We then proposed hybrid CNN models that combined U-net and SegNet features to perform image segmentation. As the NGL in the image were relatively small and tiny targets, in terms of data characteristics, they also belonged to the problem of imbalanced data. Therefore, this paper further proposed 3-Layer SegNet, 3-Layer U-SegNet, 2-Layer U-SegNet, and 2-Layer Conv-U-SegNet architectures to reduce the pooling degree of traditional semantic segmentation models, and used a loss function to increase the weight of the NGL. According to the experimental results, our proposed algorithms were indeed helpful for the image segmentation of NGL and could achieve better kappa results by 0.743.

scholarly journals WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models

2021 ◽  
Vol 13 (3) ◽  
pp. 394
Author(s):  
Wei Zhang ◽  
Ping Tang ◽  
Thomas Corpetti ◽  
Lijun Zhao
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Supervised Learning ◽  
Region Growing ◽  
Semantic Segmentation ◽  
Land Cover Classification ◽  
Training Set ◽  
Seeded Region Growing ◽  
Class Labels ◽  
Segmentation Models

Land cover classification is one of the most fundamental tasks in the field of remote sensing. In recent years, fully supervised fully convolutional network (FCN)-based semantic segmentation models have achieved state-of-the-art performance in the semantic segmentation task. However, creating pixel-level annotations is prohibitively expensive and laborious, especially when dealing with remote sensing images. Weakly supervised learning methods from weakly labeled annotations can overcome this difficulty to some extent and achieve impressive segmentation results, but results are limited in accuracy. Inspired by point supervision and the traditional segmentation method of seeded region growing (SRG) algorithm, a weakly towards strongly (WTS) supervised learning framework is proposed in this study for remote sensing land cover classification to handle the absence of well-labeled and abundant pixel-level annotations when using segmentation models. In this framework, only several points with true class labels are required as the training set, which are much less expensive to acquire compared with pixel-level annotations through field survey or visual interpretation using high-resolution images. Firstly, they are used to train a Support Vector Machine (SVM) classifier. Once fully trained, the SVM is used to generate the initial seeded pixel-level training set, in which only the pixels with high confidence are assigned with class labels whereas others are unlabeled. They are used to weakly train the segmentation model. Then, the seeded region growing module and fully connected Conditional Random Fields (CRFs) are used to iteratively update the seeded pixel-level training set for progressively increasing pixel-level supervision of the segmentation model. Sentinel-2 remote sensing images are used to validate the proposed framework, and SVM is selected for comparison. In addition, FROM-GLC10 global land cover map is used as training reference to directly train the segmentation model. Experimental results show that the proposed framework outperforms other methods and can be highly recommended for land cover classification tasks when the pixel-level labeled datasets are insufficient by using segmentation models.

scholarly journals Semantic Segmentation of Cabbage in the South Korea Highlands with Images by Unmanned Aerial Vehicles

2021 ◽  
Vol 11 (10) ◽  
pp. 4493
Author(s):  
Yongwon Jo ◽  
Soobin Lee ◽  
Youngjae Lee ◽  
Hyungu Kahng ◽  
Seonghun Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
South Korea ◽  
Unmanned Aerial Vehicles ◽  
Rapid Development ◽  
Significant Proportion ◽  
Semantic Segmentation ◽  
Training Data ◽  
Field Surveys ◽  
Aerial Vehicles ◽  
Segmentation Models

Identifying agricultural fields that grow cabbage in the highlands of South Korea is critical for accurate crop yield estimation. Only grown for a limited time during the summer, highland cabbage accounts for a significant proportion of South Korea’s annual cabbage production. Thus, it has a profound effect on the formation of cabbage prices. Traditionally, labor-extensive and time-consuming field surveys are manually carried out to derive agricultural field maps of the highlands. Recently, high-resolution overhead images of the highlands have become readily available with the rapid development of unmanned aerial vehicles (UAV) and remote sensing technology. In addition, deep learning-based semantic segmentation models have quickly advanced by recent improvements in algorithms and computational resources. In this study, we propose a semantic segmentation framework based on state-of-the-art deep learning techniques to automate the process of identifying cabbage cultivation fields. We operated UAVs and collected 2010 multispectral images under different spatiotemporal conditions to measure how well semantic segmentation models generalize. Next, we manually labeled these images at a pixel-level to obtain ground truth labels for training. Our results demonstrate that our framework performs well in detecting cabbage fields not only in areas included in the training data but also in unseen areas not included in the training data. Moreover, we analyzed the effects of infrared wavelengths on the performance of identifying cabbage fields. Based on the results of our framework, we expect agricultural officials to reduce time and manpower when identifying information about highlands cabbage fields by replacing field surveys.

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