PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network

Cheng, Jianda; Zhang, Fan; Xiang, Deliang; Yin, Qiang; Zhou, Yongsheng; Wang, Wei

doi:10.3390/rs13163132

Cited by 21 publications

(5 citation statements)

References 53 publications

(69 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Semantic segmentation assigns the class to each pixel on whatever image object or location it relates; these techniques recognize and locate items in an image and provide specific regions and limits for feature extraction. Segmentation models without considering pre-trained learnings were applied to SAR data for this research: U-Net (Captures finer information), Deeplab V3+ (Captures contextual information as well as location/spatial information), fully convolutional (Use local information), and SegNet (Improve boundary delineation)( [57,60,79]).…”

Section: Land Cover Classification Modelsmentioning

confidence: 99%

Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat

Jain

Varghese²,

Joshi

et al. 2023

Preprint

View full text Add to dashboard Cite

Both spatial and non-spatial data are critical to a country's social and economic development. Land use and land cover (LULC) maps are data that can be used to retrieve land information and detect environmental changes. Several recent studies have used deep learning approaches to generate LULC maps from various remote-sensing image formats. They have correctly recognized several land cover elements such as built-up areas, forests, water, etc. On the other hand, spaceborne radar images must be used to test deep learning methods because radar sensors operate in all weather and lighting circumstances and have diverse image characteristics depending on the wavelength. Although many researchers have applied deep learning techniques to classify the land cover features, most of them need to do more by taking less or more several land cover features and taking proper evaluation metrics to justify the classification techniques. The current research shows segmentation on Sentinel-1 C-band synthetic aperture radar (SAR) images of Gujarat's Ahmedabad, Anand, and Kheda regions using four deep-learning models. U-Net, SegNet, FCN, and DeepLabV3+ have primarily been employed in prior research to classify SAR and other satellite image formats and showed excellent results in identifying different land cover features. Training samples of five primary classes for ground-truth data were obtained using Google Earth and Sentinel-2 images as reference maps. For segmenting the Sentinel-1 image, above mentioned four deep-learning semantic segmentation models were chosen and fine-tuned. All models did exceptionally well, with overall accuracy ranging from 76% to 90%. U-Net fared well, followed by DeepLabV3+ in classifying all five land cover features, with mean intersection over union (IoU) of 0.67 and 0.55, respectively. The results reveal that using a self-generated reference map, the Sentinel-1 dual-pol images can categorize the primary land cover features.

show abstract

Section: Land Cover Classification Modelsmentioning

confidence: 99%

Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat

Jain

Varghese²,

Joshi

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Synthetic aperture radar (SAR) is an all-weather and all-time remote imaging sensor that has been widely used in many fields, such as ocean monitoring [1], agricultural development [2], and disaster prevention [3]. Ship detection in SAR images is of great importance in both military and commercial applications [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Inshore Ship Detection in Large-Scale SAR Images Based on Saliency Enhancement and Bhattacharyya-like Distance

et al. 2022

Self Cite

View full text Add to dashboard Cite

While the detection of offshore ships in synthetic aperture radar (SAR) images has been widely studied, inshore ship detection remains a challenging task. Due to the influence of speckle noise and the high similarity between onshore buildings and inshore ships, the traditional methods are unable to achieve effective detection for inshore ships. To improve the detection performance of inshore ships, we propose a novel saliency enhancement algorithm based on the difference of anisotropic pyramid (DoAP). Considering the limitations of IoU in small-target detection, we design a detection framework based on the proposed Bhattacharyya-like distance (BLD). First, the anisotropic pyramid of the SAR image is constructed by a bilateral filter (BF). Then, the differences between the finest two scales and the coarsest two scales are used to generate the saliency map, which can be used to enhance ship pixels and suppress background clutter. Finally, the BLD is used to replace IoU in label assignment and non-maximum suppression to overcome the limitations of IoU for small-target detection. We embed the DoAP into the BLD-based detection framework to detect inshore ships in large-scale SAR images. The experimental results on the LS-SSDD-v1.0 dataset indicate that the proposed method outperforms the basic state-of-the-art detection methods.

show abstract

“…DL has a strong capability to learn a series of abstract hierarchical features from raw input data and obtain a task-specific output. Therefore, DL technology provides an entirely new method for PolSAR image classification, and many classification methods have been proposed, such as Wishart deep belief network (WDBN) [21], Wishart-auto-encode (WAE) [22][23][24], sparse autoencoder [25,26], the long short-term memory (LSTM) network [27], semisupervised deep learning model [28,29], deep reinforcement learning [30], and convolutional neural network (CNN) [31][32][33]. CNN has made impressive achievements in the field of PolSAR image classification among these DL methods, and it consists of several successive convolution layers, pooling layers, and fully-connected layers.…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Multi-Scale Fully Convolutional Network with Stacked-Dilated Convolution for PolSAR Image Classification

2022

View full text Add to dashboard Cite

Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which is designed for pixel-to-pixel tasks, has obtained enormous success in semantic segmentation. Therefore, effectively using the FCN model combined with polarimetric characteristics for PolSAR image classification is quite promising. This paper proposes a novel FCN model by adopting complex-valued domain stacked-dilated convolution (CV-SDFCN). Firstly, a stacked-dilated convolution layer with different dilation rates is constructed to capture multi-scale features of PolSAR image; meanwhile, the sharing weight is employed to reduce the calculation burden. Unfortunately, the labeled training samples of PolSAR image are usually limited. Then, the encoder–decoder structure of the original FCN is reconstructed with a U-net model. Finally, in view of the significance of the phase information for PolSAR images, the proposed model is trained in the complex-valued domain rather than the real-valued domain. The experiment results show that the classification performance of the proposed method is better than several state-of-the-art PolSAR image classification methods.

show abstract

PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network

Cited by 21 publications

References 53 publications

Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat

Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat

Inshore Ship Detection in Large-Scale SAR Images Based on Saliency Enhancement and Bhattacharyya-like Distance

Complex-Valued Multi-Scale Fully Convolutional Network with Stacked-Dilated Convolution for PolSAR Image Classification

Contact Info

Product

Resources

About