Query: International Geoscience and Remote Sensing Symposium     Semantic Segmentation

title: Semantic Segmentation for High Resolution Remote Sensing Images by Light Weight Network
abstract: Accurate segmentation of high resolution remote sensing images is increasingly demanded, yet poses significant challenges for algorithm efficiency. Most current approaches pursue accuracy by employing global context information to enhance the overall consistency or utilize multi scale features or attention mechanisms to optimize object details, without considering the network complexity uniformly. In this paper, we propose a light weight semantic segmentation network for HRRS images by way of explicitly supervising the objects' body and edge features to optimize the overall consistency and object details of semantic segmentation at the same time. Furthermore, we introduce a score based feature fusion module to establish the long range dependency between pixels in the final stage of feature fusion (to combine the body and edge features) effectively. Experiments on ISPRS Vaihingen dataset show an obvious advantage of the proposed approach compared with the existing approaches. Specifically, it achieves 89.60% overall accuracy with only 2.83M parameters and 2.37GFLOPs computation costs.

title: Dual Lightweight Network with Attention and Feature Fusion for Semantic Segmentation of High Resolution Remote Sensing Images
abstract: Semantic segmentation of high resolution remote sensing (HRRS) images has been a long term research topic in the field of remote sensing. Nowdays, many excellent networks based on deep learning have been applied in various remote sensing fields. However, these networks always have a large number of network parameters and rely on extensive computing resources. To solve the above problems, we propose a lightweight dual branches network with the attention modules and the feature fusion module. The backbone networks of dual branches, which have fewer parameters, are used to obtain the detail information and the context information respectively. The attention modules are used to establish full image dependencies over the local feature representations. The feature fusion module is used to fuse the low leve features and the high leve features effectively. Compared to other popular networks, our network has better results evaluated on ISPRS Vaihingen Dataset while with fewer parameters(8M)

title: V2RNet: An Unsupervised Semantic Segmentation Algorithm for Remote Sensing Images via Cross Domain Transfer Learning
abstract: The dependence on large scale pixel level annotations brings great challenge to semantic segmentation task for remote sensing images (RSIs) To alleviate this issue, we propose V2RNet, an unsupervised semantic segmentation method which introduces adversarial learning into segmentation network. Our method creatively transfers the segmentation model from the synthetic GTA V data to the real optical remote sensing data via domain adaptation. Additionally, to unify the source domain semantic structures and target domain image style, we design a semantic segmentation discriminator as auxiliary to optimize the domain adaptation efficiency. Thus the proposed method is effective on typical remote sensing targets such densely arranged, intertwined road. Experimental results on Massachusetts Road data set demonstrate our unsupervised semantic segmentation model achieves comparable segmentation accuracy, which also validates the effectiveness of the proposed method.

title: Semantic Segmentation of Remote Sensing Images Combining Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks
abstract: In this paper, a novel method to deal with the semantic segmentation of very high resolution remote sensing data is presented. Recent advances in deep learning (DL) especially convolutional neural networks (CNNs) and fully convolutional networks (FCNs) have shown outstanding performances in this task. However, the map accuracy depends on the quantity and quality of ground truth (GT) used to train them. At the same time, probabilistic graphical models (PGMs) have sparked even more interest in the past few years, because of the ever growing need for structured predictions. The novel method proposed in this paper combines DL and PGMs to perform remote sensing image classification. FCNs can be exploited to deal with multiscale data through the integration with a hierarchical Markov model. The marginal posterior mode (MPM) criterion for inference is used in the proposed framework. Experimental validation is conducted on the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset. The results are significant, as the proposed method has a higher recall than the standard FCNs considered and allows mitigating the impact of incomplete or suboptimal GT, especially with regard to the discrimination of minoritary classes.

title: Efficient Semantic Segmentation Method with Strip Pooling for VHR Remote Sensing Images
abstract: In this paper, we address the problem of multi class semantic segmentation of high resolution remote sensing images with a deep convolutional neutral network based model named Strip Pooling Network(SPNet) The objects in high resolution remote sensing images vary greatly in size. Moreover, these objects have different extensibility and directions, such as long narrow roads and wide grasslands. These bring big challenge for traditional square pooling kernels. If we want to capture the long rang dependencies and enough contextual information, we need to use large pooling kernel which definitely increases computation greatly and incorporates interference information from irrelevant regions. SPNet introduces a new pooling strategy, called strip pooling which uses a long but narrow kernel, i.e. $1 \times \mathrm{N} or \mathrm{N}\times 1$ It can solve the above problem while preventing information from irrelevant regions. Meanwhile, two paths in the strip pooling module focus on the horizontal and vertical spatial dimensions respectively, which compensate for the lack of one path captured in the narrow dimension of each other. Experimental results on public available Potsdam dataset demonstrate that SPNet obtains an overall accuracy of 89.1% which outperforms other state of the art methods.

title: Woodland Segmentation of Gaofen 6 Remote Sensing Images Based on Deep Learning
abstract: Gaofen 6 (GF 6) is a geostationary, earth observation satellite, rely on it's multi spectral images, GF 6 has the ability to support the monitoring of woodland resources. In this paper, the multi spectral images sent by GF 6 are studied as dataset, and a model called Infrared Attention Network (InfAttNet) which based on semantic segmentation method is proposed to distinguish woodland from other land types to achieve the purpose of woodland extraction. To make full use of the spectral information, InfAttNet has an additional encoder to extract the features of infrared bands independently. Besides, infrared attention blocks help InfAttNet to enhance the characteristics of woodland. The experimental results proved that InfAttNet improves the accuracy of woodland extraction, and the segmentation effect is strengthened compared with classical networks.

title: Semantic Segmentation of PolSAR Images for Various Land Cover Features
abstract: Land cover classification is one of the core applications in the field of remote sensing. It is a valuable resource for city planners to achieve sustainable development. Many metropolitan cities are experiencing disorganized growth with a high intensity of urban sprawl due to the economic pull and better standards of living offered in metropolitan cities when compared to the surrounding rural areas. If this pattern of growth continues, it will lead to unsustainable development. This leads to an increase in pressure on urban infrastructure and the ecosystem. The traditional methods which are used for urban mapping are time consuming. Instead, Microwave Remote Sensing can be used to acquire geographical data which can be used to develop a decision support system to help urban settlement planners. This paper suggests the use of Semantic Segmentation to extract the various land cover features from Polarimetric Synthetic Aperture Radar (PolSAR) images using the Fully Convolutional Network (FCN) based modified UNet architecture, that will help in the analysis of land cover in areas prone to urban sprawl by utilizing the elements of coherency matrix.

title: Semantic Segmentation of Land Use Land Cover (LU/LC) Types Using F CNNS on Multi Sensor (Radar Ir Optical) Image Data
abstract: Land Use/ Land Cover (LU/LC) segmentation is a widely studied topic in the field of remote sensing. Past focus has been on independent studies either on color (RGB) and the Normalized Vegetation Index (NDVI) or on Polarimetric Synthetic Aperture Radar (PolSAR) data. In this paper we explore the fusion potential of RGB images with additional SAR and Near Infra red (NIR) images for enhanced LU/LC segmentation through Fully Convolutional Neural Networks (F CNNs) F CNNs have been extensively studied for semantic segmentation problems with U Net and SegNet being two well known F CNN architectures. Both these architectures were used as references for this study. High resolution RGB, SAR and NIR images were acquired through Google Earth (GE) German Aerospace Center (DLR) and The Planet Laboratories, respectively. IR was converted to NDVI for its higher potential of segmentation of vegetations areas. Four multi sensor configurations as input channels to the networks were studied after precise co registration of these images, and the results were compared to individual channels for both architectures. Simon Fraser University (SFU) Burnaby Campus and its surrounding area was selected for this study due its diverse land types. The area was divided into 5 classes i.e. Roads, Buildings, Forest, Water and No class (unclassified) An overall, best accuracy of ~86% was achieved for a five channel configuration (R+G+B+SAR+NDVI) We show that the inclusion of SAR and IR channels to RGB based network can significantly improve the performance of LU/LC segmentation.

title: GCN Based Semantic Segmentation Method for Mine Information Extraction in GAOFEN 1 Imagery
abstract: Mine information extraction is of great significance to the construction of ecological civilization, the dynamic monitoring of mine development and the scientific management of mineral resources. With the emergence of high spatial resolution remote sensing imagey, traditional machine learning method gradually cannot meet the increasing demands of image interpretation. CNN based semantic segmentation method provides a great solution for this issue. With the deepening of network layers, more the high level features can be obtained, which brings the outstanding performance of many computer vision tasks, but also leads to the loss of structural information, which is crucial for mine information extraction. Therefore, in order to improve these drawbacks, we proposed a novel network based on the classical semantic segmentation network, SegNet, and Graph Convolutional Network (GCN) that makes our method more sensitive to structural information. Then, taking the iron mine located in Qian'an City, Hebei Province as experimental area, we employed our method to extract five mainly mine objects: stopes, ore heap, waste dump, tailings reservoir and concentration based on GF 1 imagery. Compared with SegNet, the mIoU of our method was improved by about 5% on our dataset and was improved by about 2.2% on PASCAL VOC2012 dataset.

title: Unsupervised Domain Adaptation for Semantic Segmentation via Self Supervision
abstract: Recently, deep learning (DL) methods have been widely used for semantic segmentation of remote sensing and achieved significant progress. However, DL based methods are time consuming and labor intensive for the networks requiring abundant data with accurate labeling. To solve this issue, unsupervised domain adaption (UDA) has recently been used to transfer the information from labeled source domain to unlabeled target domain. In this paper, we propose a novel UDA approach based on the self supervised theory for remote sensing image. Specifically, we firstly utilize the inter domain adaptation to reduce the gap between the source and target domain. Secondly, based on our proposed spatial frequency (SF) index, we detach the target domain into an easy and hard split. Ultimately, we adopt the intra domain adaptation by self supervised adaptation to improve the performance of hard split. Experimental results on ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness and rationality of our methods against the other state of the art approaches.