Deep convolutional neural networks for land-cover classification with Sentinel-2 images

Kroupi, Eleni; Kesa, Maria; Navarro-Sanchez, Victor D.; Salman, Saad; Pelloquin, Camille; Alhaddad, Bahaa Eddin; Moreno, Laura; Soria-Frisch, Aureli; Ruffini, Giulio

doi:10.1117/1.jrs.13.024525

Cited by 19 publications

(15 citation statements)

References 42 publications

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“…In general, the FCN methods performed excellently in land cover classification. This is consistent with many results by researchers in the mapping field [7,[25][26][27][28][29]. Spatial features in remotely sensed data are very important in classification and are intrinsically local and spatially invariant.…”

Section: Discussionsupporting

confidence: 92%

Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images

Han

Dian

Xia

et al. 2020

IJGI

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Land cover is an important variable of the terrestrial ecosystem that provides information for natural resources management, urban sprawl detection, and environment research. To classify land cover with high-spatial-resolution multispectral remote sensing imagery is a difficult problem due to heterogeneous spectral values of the same object on the ground. Fully convolutional networks (FCNs) are a state-of-the-art method that has been increasingly used in image segmentation and classification. However, a systematic quantitative comparison of FCNs on high-spatial-multispectral remote imagery was not yet performed. In this paper, we adopted the three FCNs (FCN-8s, Segnet, and Unet) for Gaofen-2 (GF2) satellite imagery classification. Two scenes of GF2 with a total of 3329 polygon samples were used in the study area and a systematic quantitative comparison of FCNs was conducted with red, green, blue (RGB) and RGB+near infrared (NIR) inputs for GF2 satellite imagery. The results showed that: (1) The FCN methods perform well in land cover classification with GF2 imagery, and yet, different FCNs architectures exhibited different results in mapping accuracy. The FCN-8s model performed best among the Segnet and Unet architectures due to the multiscale feature channels in the upsampling stage. Averaged across the models, the overall accuracy (OA) and Kappa coefficient (Kappa) were 5% and 0.06 higher, respectively, in FCN-8s when compared with the other two models. (2) High-spatial-resolution remote sensing imagery with RGB+NIR bands performed better than RGB input at mapping land cover, and yet the advantage was limited; the OA and Kappa only increased an average of 0.4% and 0.01 in the RGB+NIR bands. (3) The GF2 imagery provided an encouraging result in estimating land cover based on the FCN-8s method, which can be exploited for large-scale land cover mapping in the future.

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Section: Discussionsupporting

confidence: 92%

Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images

Han

Dian

Xia

et al. 2020

IJGI

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“…With the developments in neural networks and deep learning techniques, deep Convolutional Neural Networks (CNN) have proved to be successful in different land use and land cover classification tasks. Deep learning techniques have been widely applied and have proven performance in identifying fine features from satellite images in both supervised and unsupervised ways (Basu et al (2019), Kroupi et al (2019)), and Helber et al (2019) used a patch-based classification technique with CNN by creating a EuroSAT dataset of 10 land use/land cover classes with Sentinel-2 images. The dataset consisted of around 2000 ~ 3000 images for each class covering around 34 European countries with a combination of 13 multi-spectral bands and RGB bands.…”

Section: Classificationmentioning

confidence: 99%

Deep convolutional neural networks for surface coal mines determination from sentinel-2 images

Madhuanand

Sadavarte

Visschedijk

et al. 2021

European Journal of Remote Sensing

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Coal is a principal source of energy and the combustion of coal supplies around one-third of the global electricity generation. Coal mines are also an important source of CH 4 emissions, the second most important greenhouse gas. Monitoring CH 4 emissions caused by coal mining using earth observation will require the exact location of coal mines. This paper aims to determine surface coal mines from satellite images through deep learning techniques by treating them as a land use/land cover classification task. This is achieved using Convolutional Neural Networks (CNN) that has proven to be capable of complex land use/ land cover classification tasks. With a list of known coal mine locations from various countries, a training dataset of "Coal Mine" and "No Coal Mine" image patches is prepared using Sentinel-2 satellite images with 13 spectral bands. Various pre-trained CNN network architectures (VGG, ResNet, DenseNet) are trained and validated with our prepared coal mine dataset of 3500 "Coal Mine" and 3000 "No Coal Mine" image patches. After several experiments with the VGG network combined with transfer learning is found to be an optimal model for this task. Classification accuracy of 98% has been achieved for the validation dataset of the pre-trained VGG architecture. The model produces more than 95% overall accuracy when tested on unseen satellite images from different countries outside the training dataset and evaluated against visual classification.

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“…As a result, timely collection and the analysis of data from large crop areas is of great interest. Traditionally, such analysis is carried out by using computational tools and satellite imagery processing with artificial intelligence (AI) techniques [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Convolutional-Neural-Network Architecture for Crop Classification

et al. 2021

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Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.

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Deep convolutional neural networks for land-cover classification with Sentinel-2 images

Cited by 19 publications

References 42 publications

Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images

Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images

Deep convolutional neural networks for surface coal mines determination from sentinel-2 images

Enhanced Convolutional-Neural-Network Architecture for Crop Classification

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