Automatic coastline extraction through enhanced sea-land segmentation by modifying Standard U-Net

Aghdami-Nia, Mohammad; Shah-Hosseini, Reza; Rostami, Amirhossein; Homayouni, Saeid

doi:10.1016/j.jag.2022.102785

Cited by 31 publications

(20 citation statements)

References 30 publications

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“…As discussed in Sec. 2, several well-received methods are selected to conduct the comparative study under the same experimental environment, including Otsu, 11 FCN, 18 U-Net, 19 SegNet, 20 DeepUNet, 26 PSPNet, 21 DFANet, 22 DeepLabV3+, 24 SANet, 9 and MSUNet 27 …”

Section: Methodsmentioning

confidence: 99%

HeteroNet: a heterogeneous encoder–decoder network for sea–land segmentation of remote sensing images

Ji,

Tang,

Liu

et al. 2023

J. Electron. Imag.

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.The research on sea–land segmentation of remote sensing images has received tremendous attention, which is of great significance to coastline extraction and ocean monitoring. In recent years, various convolutional neural networks (CNNs) have been presented to achieve precise and efficient sea–land segmentation effect. However, existing CNNs typically adopt the symmetric encoder-decoder structure, which is inefficient for feature extraction, feature fusion, and information transmission. To address these problems, this work develops a CNN for pixel-level sea–land segmentation, termed HeteroNet. The proposed HeteroNet constructs a heterogeneous encoder–decoder structure consisting of successive dense-connected encoding modules and squeeze-and-excitation-connected decoding modules that can effectively enhance the feature extraction and fusion capabilities of the network. In addition, an easy-to-embed global context enhanced module is designed to further facilitate information transmission efficiency. Comparative experiments with state-of-the-art methods are conducted to reveal that the HeteroNet can exhibit superior sea–land segmentation performance in different scenarios, and the ablation study is performed to demonstrate the effectiveness of each component in the network.

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Section: Methodsmentioning

confidence: 99%

HeteroNet: a heterogeneous encoder–decoder network for sea–land segmentation of remote sensing images

Ji,

Tang,

Liu

et al. 2023

J. Electron. Imag.

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show abstract

“…For example, Liu et al(2018) used eight MODIS coarse resolution images to compute EVI in the growing season of Henan province of China and achieved the OA of 84% by the global threshold model. The ML and deep learning algorithm can develop a model that simulates the relation between output and different variables as inputs (Aghdami-Nia et al, 2022;Rostami et al, 2022b). Nevertheless, ML algorithms have parameters that should be optimized with some methods (Ansari and Akhoondzadeh, 2020;Ranjbar et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Machine Learning Classifiers for Crop Type Mapping Using Vegetation Indices

Asgari

Hasanlou²

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Timely and accurate mapping of crops is crucial for agriculture management, policy-making, and food security. Due to the differences in the product calendars of various crops, it is possible to classify them by investigating the remote sensing Vegetation Indices (VIs) during crop growth season. This study developed a VI-based mapping approach to specifying crop types based on phenological and spectral metrics derived from the sentinel-2 images. We used six spectral VIs (ARVI, CVI, EVI, LAI, GLI, and NDVI) in three supervised machine learning methods, including Random Forest (RF), GBoost (GB), and K-Nearest Neighborhood (KNN) for crop mapping. Field data consisting of wheat, barley, canola, vegetables, and a bare land class, were collected as the testing and training data set. The classification results were evaluated through test samples showing high overall accuracy (OA) and satisfactory class accuracies for the most dominant crop types across different fields despite the variability of planting and harvesting dates. Among the VIs utilized to crop mapping, the Atmospherically Resistant Vegetation Index (ARVI) in all three classification methods achieved better results. The overall accuracy of RF, GB, and KNN models with the ARVI index was 95%, 88%, and 90%, respectively.

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“…This method for CD is usually considered a pre-processing step for more complex frameworks where change classes are also detected. Deep learning (Aghdami-Nia et al, 2022;Ansari et al, 2021;Rostami et al, 2022b) and machine learning (Ranjbar et al, 2021;Rostami et al, 2022a;Zarei et al, 2021) methods have dominated many RS fields and CD is no exception. Siamese networks have attracted special attention for CD purposes among researchers in recent years.…”

Section: Introductionmentioning

confidence: 99%

Effect of Transferring Pre-Trained Weights on a Siamese Change Detection Network

Aghdami-Nia

Shah-Hosseini

Salmani

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Change Detection (CD) is one of the most crucial applications in remote sensing which identifies meaningful changes from bitemporal images taken from the same location. Enhancing the temporal efficiency and accuracy of this task is of great importance and one way to achieve this is through transfer learning. In this study, we investigate the influence of transferring pre-trained weights on the performance of a Siamese CD network using a benchmark dataset. For this purpose, an autoencoder with the same encoder architecture as in the Siamese model is trained on the whole dataset. Then, the encoder weights are transferred from the autoencoder and the Siamese model is trained in two modes. In the first mode, the transferred weights are frozen and only the decoder section of the Siamese models is trained while the second mode trains the whole model without freezing any part of the model. Moreover, the Siamese model is also trained without using the pre-trained weights to set the basis for comparisons. The results indicate that freezing the encoder results in a relatively lower performance but offers a considerable amount of temporal efficiency in the training phase. On the other hand, training the whole model after the weight transfer acquires the best result with an improvement of 12.43% in the Intersection over Union (IoU) metric.

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Automatic coastline extraction through enhanced sea-land segmentation by modifying Standard U-Net

Cited by 31 publications

References 30 publications

HeteroNet: a heterogeneous encoder–decoder network for sea–land segmentation of remote sensing images

HeteroNet: a heterogeneous encoder–decoder network for sea–land segmentation of remote sensing images

A Comparative Study of Machine Learning Classifiers for Crop Type Mapping Using Vegetation Indices

Effect of Transferring Pre-Trained Weights on a Siamese Change Detection Network

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