Automatic land cover classification of multi-resolution dualpol data using convolutional neural network (CNN)

In classification of satellite images acquired over smallholder agricultural landscape with complex spectral profiles of various crop types, exploring image spatial information is important. The deep convolutional neural network (CNN), originally designed for natural image recognition in the computer vision field, can automatically explore high level spatial information and thus is promising for such tasks. This study tried to evaluate different CNN structures for classification of four smallholder agricultural landscapes in Heilongjiang, China using pan-sharpened 2 m GaoFen-1 (meaning high resolution in Chinese) satellite images. CNN with three pooling strategies: without pooling, with max pooling and with average pooling, were evaluated and compared with random forest. Two different numbers (~70,000 and ~290,000) of CNN learnable parameters were examined for each pooling strategy. The training and testing samples were systematically sampled from reference land cover maps to ensure sample distribution proportional to the reference land cover occurrence and included 60,000–400,000 pixels to ensure effective training. Testing sample classification results in the four study areas showed that the best pooling strategy was the average pooling CNN and that the CNN significantly outperformed random forest (2.4–3.3% higher overall accuracy and 0.05–0.24 higher kappa coefficient). Visual examination of CNN classification maps showed that CNN can discriminate better the spectrally similar crop types by effectively exploring spatial information. CNN was still significantly outperformed random forest using training samples that were evenly distributed among classes. Furthermore, future research to improve CNN performance was discussed.

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Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta

Remote Sensing ◽

10.3390/rs11091006 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1006 ◽

Cited By ~ 16

Author(s):

Quanlong Feng ◽

Jianyu Yang ◽

Dehai Zhu ◽

Jiantao Liu ◽

Hao Guo ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Deep Learning ◽

Land Cover ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Yellow River ◽

Rapid Development ◽

Land Cover Classification ◽

Coastal Land

Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy.

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A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2018.08.005 ◽

2018 ◽

Vol 144 ◽

pp. 423-434 ◽

Cited By ~ 11

Author(s):

Zewei Xu ◽

Kaiyu Guan ◽

Nathan Casler ◽

Bin Peng ◽

Shaowen Wang

Keyword(s):

Neural Network ◽

Land Cover ◽

Convolutional Neural Network ◽

Landsat Imagery ◽

Land Cover Classification ◽

Neural Network Method ◽

Network Method ◽

Multi Temporal

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QUALITY EVALUATION OF LAND-COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-257-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 257-262 ◽

Cited By ~ 1

Author(s):

Y. Dang ◽

J. Zhang ◽

Y. Zhao ◽

F. Luo ◽

W. Ma ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Land Cover ◽

Convolutional Neural Network ◽

Land Surface ◽

Quality Evaluation ◽

Remote Sensing Data ◽

Land Cover Classification ◽

Sensing Data ◽

Evaluation Approach

Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can’t be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image’s membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation of DLG-DOM coupling land-cover classification or other kinds of labelled remote sensing data can be further studied.

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