Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Mahdianpari, Masoud; Salehi, Bahram; Rezaee, Mohammad; Mohammadimanesh, Fariba; Zhang, Yun

doi:10.3390/rs10071119

Cited by 327 publications

(203 citation statements)

References 40 publications

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“…Pouliot et al [51] tested a CNN wetland classification over a similar region in Alberta using Landsat data and reported 69% overall accuracy. Mahdianpari et al [52] achieved a 96% wetland class accuracy in Newfoundland with RapidEye data and an InceptionResNetV2 algorithm. Mohammadimanesh et al [20] reported a 93% wetland class accuracy again in Newfoundland using RADARSAT-2 data and a fully convolutional neural network.…”

Section: Discussionmentioning

confidence: 99%

“…For example, waterline edges which delineate marshes and open water may only need simple edge detection convolution filters, while fens and bogs may be differentiated by subtle variations in texture or color (i.e., visible flow lines in fens). Within the last couple of years, a number of studies have attempted to use deep learning for wetland mapping in Canada over small areas and have achieved promising results when compared to alternative shallow learning methods [20,51,52].…”

Section: Introductionmentioning

confidence: 99%

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Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada

Simms²,

et al. 2019

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Advances in machine learning have changed many fields of study and it has also drawn attention in a variety of remote sensing applications. In particular, deep convolutional neural networks (CNNs) have proven very useful in fields such as image recognition; however, the use of CNNs in large-scale remote sensing landcover classifications still needs further investigation. We set out to test CNN-based landcover classification against a more conventional XGBoost shallow learning algorithm for mapping a notoriously difficult group of landcover classes, wetland class as defined by the Canadian Wetland Classification System. We developed two wetland inventory style products for a large (397,958 km2) area in the Boreal Forest region of Alberta, Canada, using Sentinel-1, Sentinel-2, and ALOS DEM data acquired in Google Earth Engine. We then tested the accuracy of these two products against three validation data sets (two photo-interpreted and one field). The CNN-generated wetland product proved to be more accurate than the shallow learning XGBoost wetland product by 5%. The overall accuracy of the CNN product was 80.2% with a mean F1-score of 0.58. We believe that CNNs are better able to capture natural complexities within wetland classes, and thus may be very useful for complex landcover classifications. Overall, this CNN framework shows great promise for generating large-scale wetland inventory data and may prove useful for other landcover mapping applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada

Simms²,

et al. 2019

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“…Besides pixel to pixel intensity association, CNNs utilize spatial association of neighboring pixels for classification and regression tasks. Using the combinations of convolutional layers and max pooling layers, CNN gets enhanced accuracy in classification tasks and usually outperforms the traditional machine learning models like support vector machines and random forests [48,49]. CNNs have several major components i.e., convolutional layers, pooling layers, dropout layers and fully connected layers.…”

Section: Cnn Model Developmentmentioning

confidence: 99%

Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks

Shah

Manzoor

Bais

2020

MAKE

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Forest structure estimation is very important in geological, ecological and environmental studies. It provides the basis for the carbon stock estimation and effective means of sequestration of carbon sources and sinks. Multiple parameters are used to estimate the forest structure like above ground biomass, leaf area index and diameter at breast height. Among all these parameters, vegetation height has unique standing. In addition to forest structure estimation it provides the insight into long term historical changes and the estimates of stand age of the forests as well. There are multiple techniques available to estimate the canopy height. Light detection and ranging (LiDAR) based methods, being the accurate and useful ones, are very expensive to obtain and have no global coverage. There is a need to establish a mechanism to estimate the canopy height using freely available satellite imagery like Landsat images. Multiple studies are available which contribute in this area. The majority use Landsat images with random forest models. Although random forest based models are widely used in remote sensing applications, they lack the ability to utilize the spatial association of neighboring pixels in modeling process. In this research work, we define Convolutional Neural Network based model and analyze that model for three test configurations. We replicate the random forest based setup of Grant et al., which is a similar state-of-the-art study, and compare our results and show that the convolutional neural networks (CNN) based models not only capture the spatial association of neighboring pixels but also outperform the state-of-the-art.

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“…Deep learning has been widely used for object detection and classification and there exist many highly performant architectures such as InceptionV3, VGG16, VGG19, ResNet50 and many more (e.g. Mahdianpari et al 2018). As we are using multispectral data and aim at classifying rocks that are very different from other objects, we cannot use networks that are pretrained on large computer vision datasets but need to train a new network that is well-suited for smaller datasets.…”

Section: Cnn Architecture and Experimentsmentioning

confidence: 99%

“…For computer vision tasks, deep-learning has been pushed by companies such as Google, Baidu, Microsoft and Facebook (Zhu et al 2017) and is becoming increasingly popular in remote sensing tasks such as object detection and classification (e.g. Kamilaris and Prenafeta-Boldú 2018;Mahdianpari et al 2018;Zhu et al 2017 and many more). However, deep learning approaches and an end-to-end integration into a GIS environment are still scarce for geological applications.…”

Section: Introductionmentioning

confidence: 99%

Lithological Classification Using Multi-Sensor Data and Convolutional Neural Networks

Brandmeier¹,

Chen

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Deep learning has been used successfully in computer vision problems, e.g. image classification, target detection and many more. We use deep learning in conjunction with ArcGIS to implement a model with advanced convolutional neural networks (CNN) for lithological mapping in the Mount Isa region (Australia). The area is ideal for spectral remote sensing as there is only sparse vegetation and besides freely available Sentinel-2 and ASTER data, several geophysical datasets are available from exploration campaigns. By fusing the data and thus covering a wide spectral range as well as capturing geophysical properties of rocks, we aim at improving classification accuracies and support geological mapping. We also evaluate the performance of the sensors on their own compared to a joint use as the Sentinel-2 satellites are relatively new and as of now there exist only few studies for geological applications. We developed an end-to-end deep learning model using Keras and Tensorflow that consists of several convolutional, pooling and deconvolutional layers. Our model was inspired by the family of U-Net architectures, where low-level feature maps (encoders) are concatenated with high-level ones (decoders), which enables precise localization. This type of network architecture was especially designed to effectively solve pixel-wise classification problems, which is appropriate for lithological classification. We spatially resampled and fused the multi-sensor remote sensing data with different bands and geophysical data into image cubes as input for our model. Pre-processing was done in ArcGIS and the final, fine-tuned model was imported into a toolbox to be used on further scenes directly in the GIS environment. The tool classifies each pixel of the multiband imagery into different types of rocks according to a defined probability threshold. Results highlight the power of using Sentinel-2 in conjunction with ASTER data with accuracies of 75% in comparison to only 70% and 73% for ASTER or Sentinel-2 data alone. These results are similar but examining the different classes shows that there are significant improvements for classes such as dolerite or carbonate sediments that are not that widely distributed in the area. Adding geophysical datasets reduced accuracies to 60%, probably due to an order of magnitude difference in spatial resolution. In comparison, Random Forest (RF) and Support Vector Machines (SVMs) that were trained on the same data only achieve accuracies of 46 % and 36 % respectively. Most insecurity is due to labelling errors and labels with mixed lithologies. However, results show that the U-Net model is a powerful alternative to other classifiers for medium-resolution multispectral data.

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Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Cited by 327 publications

References 40 publications

Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada

Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada

Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks

Lithological Classification Using Multi-Sensor Data and Convolutional Neural Networks

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