BAS$^{4}$Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images

The presence of shadows in high-resolution (HR) remote-sensing images reduces object detection accuracy. To address this problem, in this paper, we proposed a deep neural network algorithm for shadow detection by using the AISD and SSAD remote-sensing shadow image datasets. To improve the ability to extract spatial information from feature maps, we developed a cross-spatial attention module that focuses on semantic information in the horizontal and vertical directions at each position point on the remote-sensing image. This module overcomes the limitations of existing technologies in accurately judging small areas and suspected shadow areas and in missing or incorrectly detected shadow areas. In addition, to improve the ability to extract shadow features and the accuracy of shadow detection in remote-sensing images, we developed a channel attention module that assigns more attention to channels that conform to the shadow color characteristics. The network architecture comprises an encoder – decoder structure, with ResNeXt50 used as the backbone for the encoder and a multi resolution parallel fusion (MRPF) designed for the decoder; cross-spatial and channel attention were incorporated into the decoder unit. Experimental results demonstrated the superior performance of the proposed algorithm, with an F1 score of 92.6% for the shadow category on the test set, thus, outperforming other algorithms and making the proposed method an effective solution for shadow detection in HR remote-sensing images.

Uncrewed aerial vehicles (UAVs) aerial remote sensing (RS) images offer higher spatial resolution compared with satellite RS images, providing more detailed scene information. The flexibility of UAV-mounted sensors allows for the simultaneous acquisition of multimodal data, including RGB images and depth maps. Depth information, serving as a supplementary cue, has been extensively utilized in semantic segmentation research. Nevertheless, the current investigations on red, green, blue-depth (RGB-D) image semantic segmentation predominantly concentrate on indoor or road scenarios. In UAV RS scenes, there exist numerous objects featuring similar visual texture manifestations. The introduction of depth geometric information can efficaciously enhance the semantic segmentation accuracy of complex ground objects. Concurrently, high-resolution RS images also present challenges to the modeling of global information in large-scale space. To address these challenges, we propose the first framework for semantic segmentation of RGB-D images in UAV RS, namely, SkyMamba. This framework effectively integrates visual texture information and geometric depth information. Simultaneously, it leverages the long-sequence modeling capability of the state-space model to efficiently capture global context information in the RS space, achieving efficient semantic segmentation of RS scenes. Furthermore, we introduce a novel benchmark dataset for semantic segmentation of UAV RS RGB-D images, named comprehensive urban-scale depth, which is divided into two versions based on coarse-grained and fine-grained semantic annotation strategies to provide diverse challenges. Experimental results demonstrate that the proposed method exhibits state-of-the-art performance on both versions of the dataset, particularly in handling imbalanced scene semantic class distributions. The code for this article is available at the following URL: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CapyLin-G/CUS-Depth</uri>

Content-based remote sensing (RS) image retrieval (CBRSIR) is a critical way to organize high-resolution RS (HRRS) images in the current big data era. The increasing volume of HRRS images from different satellites and sensors leads to more attention to the cross-source CSRSIR (CS-CBRSIR) problem. Due to the data drift, one crucial problem in CS-CBRSIR is the modality discrepancy. Most existing methods focus on finding a common feature space for various HRRS images to address this issue. In this space, their similarity relations can be measured directly to obtain the cross-source retrieval results straight. This way is feasible and reasonable, however, the specific information corresponding to HRRS images from different sources is always ignored, limiting retrieval performance. To overcome this limitation, we develop a new model for CS-CBRSIR in this paper named dual modality collaborative learning (DMCL). To fully explore the specific information from diverse HRRS images, DMCL first introduces ResNet50 as the feature extractor. Then, a common space mutual learning module is developed to map the specific features into a common space. Here, the modality discrepancy is reduced from the aspects of features and their distributions. Finally, to supplement the specific knowledge to the common features, we develop modality transformation and the dual-modality feature learning modules. Their function is to transmit the specific knowledge from different sources mutually and fuse the specific and common features adaptively. The comprehensive experiments are conducted on a public dataset. Compared with many existing methods, the behavior of our DMCL is stronger. These encouraging results for a public dataset indicate that the proposed DMCL is useful in CS-CBRSIR tasks.

The use of deep neural networks for semantic segmentation of RS (remote sensing) images is an important research topic in the field of remote sensing intelligent interpretation, which has significant application value in urban planning, disaster assessment, carbon estimation, and other fields. However, high-resolution RS images bring problems of large data scale, high computational complexity, diverse scales, and irregular shapes. Moreover, there may be distant spatial relationships between target objects, requiring the utilization of long-range contextual information for accurate semantic segmentation. Inspired by the powerful global modelling capability of the Swin Transformer, we propose a new RS image semantic segmentation network. Swin Transformer is used as an encoder to extract global features. To prevent the loss of local details in the encoding stage, one LKA-PM (LKA-Patch Merging) module is designed to extract local detail information by decomposing large kernel convolutions, thereby enhancing the feature representation ability of small objects. Furthermore, to decode global and local features and obtain multi-scale information, one WORC (Weighted Operation Re-parameterization Convolution) module is designed to improve the adaptability and accuracy of the model to different RS images. Finally, to preserve spatial details during decoding and reduce the problem of boundary blurring caused by occlusion in RS imagery, the Coordswin module is designed to provide coordinate information and improve the discrimination ability of similar objects. Experimental results demonstrate that our CW-SwinUNet achieves excellent segmentation performance (91.8 % F1 and 85.2 % MIoU) on the Vaihingen dataset and (93.8 % F1 and 87.9 % MIoU) on Potsdam datasets, surpassing other state-of-the-art methods. The source code of the proposed CW-SwinUNet is available at https://github.com/xmy1135/cwswin/tree/main.

In recent decades, high-resolution (HR) remote sensing images have shown considerable potential for providing detailed information for change detection. The traditional change detection methods based on HR remote sensing images mostly only detect a single land type or only the change range, and cannot simultaneously detect the change of all object types and pixel-level range changes in the area. To overcome this difficulty, we propose a new coarse-to-fine deep learning-based land-use change detection method. We independently created a new scene classification dataset called NS-55, and innovatively considered the adaptation relationship between the convolutional neural network (CNN) and the scene complexity by selecting the CNN that best fit the scene complexity. The CNN trained by NS-55 was used to detect the category of the scene, define the final category of the scene according to the majority voting method, and obtain the changed scene by comparison to obtain the so-called coarse change result. Then, we created a multi-scale threshold (MST) method, which is a new method for obtaining high-quality training samples. We used the high-quality samples selected by MST to train the deep belief network to obtain the pixel-level range change detection results. By mapping coarse scene changes to range changes, we could obtain fine multi-type land-use change detection results. Experiments were conducted on the Multi-temporal Scene Wuhan dataset and aerial images of a particular area of Dapeng New District, Shenzhen, where promising results were achieved by the proposed method. This demonstrates that the proposed method is practical, easy-to-implement, and the NS-55 dataset is physically justified. The proposed method has the potential to be applied in the large scale land use fine change detection problem and qualitative and quantitative research on land use/cover change based on HR remote sensing data.

In view of the characteristics of high score remote sensing data containing rich semantic information, combined with different processing requirements for high-score remote sensing images, this paper designs and implements a parallel classification and detection system based on high-score remote sensing image. With the development of remote sensing technology and artificial intelligence technology, it is of great significance to process high-score remote sensing image data based on deep learning convolution neural network. The system integrates sea land segmentation model and target detection model, which can realize different types of data processing requirements. It can realize the high-efficiency processing of large-scale images by means of parallel processing technology, and realize the detection effect of the specified targets by visual processing technology. It is applicable to the processing tasks of high-resolution remote sensing images of all types, and has a high level of intelligent and parallel processing.

As the rapid development of earth observation technology and deep learning, building extraction from remotely sensed imagery based on deep convolutional neural networks (DCNNs) has attracted wide attention in recent years. However, due to the heterogeneity of building shapes and sizes and the complexity of the surrounding objects, current building extraction methods still have challenges in boundary accuracy and complete building extraction. For these purposes, we proposed low-level feature enhancement and multi-scale attention pyramid aggregation network (LFEMAP-Net) that considers building boundary information and multi-scale feature expression to obtain higher accuracy building extraction. Firstly, low-level feature enhancement model is proposed based on prior edge information to enhance the representation of spatial details, effectively addressing issues related to information loss and fuzzy boundaries. Additionally, a multi-scale attention pyramid aggregation model is developed during the decoding stage to facilitate the fusion of features from different scales, thereby enhancing the extraction of building features. Experimental results on two publicly available datasets validate that LFEMAP-Net can overcome building extraction interruptions and boundary blur in complex scenes, and achieve boundary optimization and complete segmentation of buildings and achieve better performance than other advanced semantic segmentation models.

In recent years, there has been a significant increase in the production of high-resolution aerial and satellite imagery. Analyzing and extracting urban features from these images, especially building components, is a significant challenge in photogrammetry and remote sensing. Meanwhile, deep convolution neural networks have been used as a powerful model in the semantic segmentation of building features. However, due to the structure of these types of deep convolution networks, accurate retrieval of building boundary information during training will be difficult, and this will lead to ambiguous boundary areas. On the other hand, the use of distributed-based loss functions, such as the binary cross-entropy loss alone cannot improve the segmentation accuracy in the boundary areas of building features. Accordingly, in this paper, a derivative boundary loss function is introduced to optimize and enhance the extraction of buildings in border areas. To calculate the proposed boundary loss function, distance transform image (DTI), is obtained from ground-truth images and predicted images, that derived from the segmentation network. In this paper, a derivative method for calculating DTI is presented. Another advantage of the proposed loss function is its ability to be used in a wide range of deep convolution segmentation networks. The proposed method was investigated and tested using the ISPRS Potsdam and Vaihingen data sets. The results of this implementation show the superiority in the criteria of evaluation and improvement of buildings extraction if the proposed boundary loss function is used along with the distributed-based loss function.

Building extraction from very high-resolution remote sensing images is a fundamental task and is widely used in applications such as change detection, disaster assessment, and real-time update of geographic information databases. However, due to the complexity of the geographical environment and the diversity of target features, accurate automatic building extraction remains very challenging. With the fast development of deep learning techniques, convolutional neural networks(CNN) have been widely used in remote sensing research and have achieved considerable results. But for large urban area-based building detection tasks, the CNN-based method usually gets into local optima and generates many false positive detections around building boundaries. To avoid the local optima and be aware of non-local information, this paper proposes a hybrid feature extraction model based on the combination of the CNN and Transformer to realize the automatic building detection from very high-resolution remote sensing images. Meanwhile, a multi-constraint weighting mechanism is proposed to enhance the ability of the model to recognize the regular geometric boundaries of buildings. Comprehensive experiments are conducted on the three different datasets. The proposed MC-TRANSU achieves the best F1-score, IoU, compared to the state-of-the-art methods such as SegNet, TransUnet, Swin-Unet, the detection accuracy improved around 5%. Quantitative and qualitative results verify the superiority and effectiveness of our model.

We develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfectly regular enclosures but also for ruined ones with distorted angles, fragmented walls, or even a completely missing wall. Furthermore, it has a zero value for spurious structures with less than three sides of a perceivable rectangle. We show how the detection performance can be improved by learning a linear combination of the rectangularity and size features from just a few available representative examples and a large number of negatives. Our approach allowed detection of enclosures in the Silvretta Alps that were previously unknown. A comparative performance analysis is provided. Among other features, our comparison includes the state-of-the-art features that were generated by pretrained deep convolutional neural networks (CNNs). The deep CNN features, although learned from a very different type of images, provided the basic ability to capture the visual concept of the LEs. However, our handcrafted rectangularity-size features showed considerably higher performance.

Given a pair of bitemporal very high resolution (VHR) remote sensing images, the semantic change detection task aims to locate land surface changes and identify their semantic classes. The existing algorithms use independent branches to locate and identify separately without considering the association between branches. In this article, we propose an end-to-end spatially and semantically enhanced Siamese network (SSESN) for semantic change detection. The SSESN aggregates the rich spatial and semantic information in the VHR image through a designed spatial and semantic feature aggregation module. Additionally, a change-aware module is proposed to decouple the aggregated features. Features in the binary branch are fused to the semantic branches as prior location information. This allows the spatially enhanced features to predict changed regions and the semantically enhanced features to refine the region categorizations. Experimental results show that our method provides comparable results with the state-of-the-art binary change detection and semantic change detection algorithms.

Enabling autonomous driving (AD) can be considered one of the biggest challenges in today's technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usually performed by combining the semantic information captured by several sensors, i.e., lidar or camera. The semantic information from the respective sensor can be extracted by using convolutional neural networks (CNNs) for dense prediction. In the past, CNNs constantly showed state-of-the-art performance on several vision-related tasks, such as semantic segmentation of traffic scenes using nothing but the red-green-blue (RGB) images provided by a camera. Although CNNs obtain state-of-the-art performance on clean images, almost imperceptible changes to the input, referred to as adversarial perturbations, may lead to fatal deception. The goal of this article is to illuminate the vulnerability aspects of CNNs used for semantic segmentation with respect to adversarial attacks, and share insights into some of the existing known adversarial defense strategies. We aim to clarify the advantages and disadvantages associated with applying CNNs for environment perception in AD to serve as a motivation for future research in this field.

Semantic segmentation is a crucial method for recognizing and classifying objects in high-resolution remote sensing images (HRRSIs). However, due to the problems of varying target scale and difficulty in determining the edges of small-scale targets in remote sensing images, traditional semantic segmentation models perform poorly. To address this issue, we propose a multi-scale feature enhancement network (MFENet) to improve the segmentation accuracy of small-scale objects in HRRSIs. MFENet considers the differences between objects of different scales and selects more suitable receptive fields to enhance the extraction of multi-scale semantic features. We propose a composite atrous multi-scale feature fusion (CAMFF) module to enhance the extraction of spatial detail and semantic information of features at different scales. In addition, we propose an improved composite atrous spatial pyramid pooling (C-ASPP) module to enhance the network feature extraction capability across multiple scales. We also propose a network structure that combines the C-ASPP module with the efficient channel attention (ECA) module in parallel, which performs better to extract contextual information. Our experimental evaluations on the Potsdam and Vaihingen datasets demonstrate the effectiveness of our Network, It F1 score reaching 93.33% and 94.66% respectively.

As remote sensing images have complex backgrounds and varying object sizes, their semantic segmentation is challenging. This study proposes a multiscale cascaded network (MSCNet) for semantic segmentation. The resolutions employed with respect to the input remote sensing images are 1, 1/2, and 1/4, which represent high, medium, and low resolutions. First, 3 backbone networks extract features with different resolutions. Then, using a multiscale attention network, the fused features are input into the dense atrous spatial pyramid pooling network to obtain multiscale information. The proposed MSCNet introduces multiscale feature extraction and attention mechanism modules suitable for remote sensing land-cover classification. Experiments are performed using the Deepglobe, Vaihingen, and Potsdam datasets; the results are compared with those of the existing classical semantic segmentation networks. The findings indicate that the mean intersection over union (mIoU) of the MSCNet is 4.73% higher than that of DeepLabv3+ with the Deepglobe datasets. For the Vaihingen datasets, the mIoU of the MSCNet is 15.3%, and 6.4% higher than those of a segmented network (SegNet), and DeepLabv3+, respectively. For the Potsdam datasets, the mIoU of the MSCNet is higher than those of a fully convolutional network, Res-U-Net, SegNet, and DeepLabv3+ by 11.18%, 5.89%, 4.78%, and 3.03%, respectively.

Over the last years, Convolutional Neural Networks (CNNs) have been widely used in remote sensing applications, such as marine surveillance, traffic management or road networks detection. However, since CNNs have extremely high computation, bandwith and memory requirements, the hardware implementation of a CNN on space-grade devices like FPGAs for the on-board processing of the acquired images has brought many challenges. In fact, FPGAs show great advantages in terms of reconfigurability and performance-power ratio, whichmakes them a suitable election to deploy CNNs, although the computational capabilities of this type of hardware devices available on-board is limited, so implementations have to be carefully planned. In this paper, the authors present their work towards the implementation of an efficient CNN onto a spacegrade FPGA in order to achieve the on-board processing of very-high resolution remotely sensed images as soon as the data are provided by the sensor. All this work has been conducted within the EU-funded VIDEO project. As it will be presented in this paper, the work includes the introduction of a methodology based on the project constraints, the evaluation of different state-of-the-art CNN architectures by means of a new efficiency measurement also proposed in this work, the introduction of a new efficient CNN architecture, and finally, its optimized hardware implementation by means of high-level synthesis tools.