The PASCAL Visual Object Classes Challenge

Semantic change detection (SCD) extends the multi-class change detection (MCD) task to provide not only the change locations but also the detailed land-cover/land-use (LCLU) categories before and after the observation intervals. This fine-grained semantic change information is very useful in many applications. Recent studies indicate that the SCD can be modeled through a triple-branch Convolutional Neural Network (CNN), which contains two temporal branches and a change branch. However, in this architecture, the communications between the temporal branches and the change branch are insufficient. To overcome the limitations in existing methods, we propose a novel CNN architecture for the SCD, where the semantic temporal features are merged in a deep CD unit. Furthermore, we elaborate on this architecture to reason the bi-temporal semantic correlations. The resulting Bi-temporal Semantic Reasoning Network (Bi-SRNet) contains two types of semantic reasoning blocks to reason both single-temporal and cross-temporal semantic correlations, as well as a novel loss function to improve the semantic consistency of change detection results. Experimental results on a benchmark dataset show that the proposed architecture obtains significant accuracy improvements over the existing approaches, while the added designs in the Bi-SRNet further improves the segmentation of both semantic categories and the changed areas. The codes in this paper are accessible at: github.com/ggsDing/Bi-SRNet.

/pdf/bi-temporal-semantic-reasoning-for-the-semantic-change-1bmobafpzo.pdf

Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images

Change Detection (CD) provides a research basis for environmental monitoring, urban expansion and reconstruction as well as disaster assessment, by identifying the changes of ground objects in different time periods. Traditional CD focused on the Binary Change Detection (BCD), focusing solely on the change and no-change regions. Due to the dynamic progress of earth observation satellite techniques, the spatial resolution of remote sensing images continues to increase, Multi-class Change Detection (MCD) which can reflect more detailed land change has become a hot research direction in the field of CD. Although many scholars have reviewed change detection at present, most of the work still focuses on BCD. This paper focuses on the recent progress in MCD, which includes five major aspects: challenges, datasets, methods, applications and future research direction. Specifically, the background of MCD is first introduced. Then, the major difficulties and challenges in MCD are discussed and delineated. The benchmark datasets for MCD are described, and the available open datasets are listed. Moreover, MCD is further divided into three categories and the specific techniques are described, respectively. Subsequently, the common applications of MCD are described. Finally, the relevant literature in the main journals of remote sensing in the past five years are analyzed and the development and future research direction of MCD are discussed. This review will help researchers understand this field and provide a reference for the subsequent development of MCD. Our collections of MCD benchmark datasets are available at: https://zenodo.org/record/6809804#.YsfvxXZByUk 

A review of multi-class change detection for satellite remote sensing imagery

Buildings are some of the basic spatial elements of a city. Changes in the spatial distributions of buildings are of great significance for urban planning and monitoring illegal construction. Building change detection (CD) with high-resolution remote sensing images based on deep learning can be used to quickly identify large-scale spatial distribution changes, saving many workforce and material resources. However, existing CD networks mainly focus on regional accuracy, ignoring the importance of accurate boundary identification. It is often difficult for CD networks to achieve accurate boundary segmentation, especially for dense and continuously distributed buildings. In addition, due to the inconsistencies among classes and the discontinuities within classes, it is difficult for CD networks to obtain complete change results. In response to the above problems, a novel method called EGDE-Net that focuses on boundary accuracy and change region integrity is proposed in this paper. First, an edge-guided Transformer block is designed to encode dual-branch networks for EGDE-Net; this block combines a hierarchical Transformer with an edge-aware module (EAM) for long-range context modeling and feature refinement. Second, a feature differential enhancement module (FDEM) is developed to learn highly discriminative change feature maps by exploiting the differences between bitemporal features. In addition, feature maps are recovered through multiple upsampling operations and dilated asymmetric modules (DAMs) in the decoding part of the network. Finally, prior information for boundaries and change information are jointly used to implement a supervision process and obtain the optimal model. The proposed EGDE-Net achieves better results based on the WHU building CD dataset and LEVIR-CD dataset than do similar methods. Notably, F1 scores of 93.02% and 90.05% and intersection over union (IoU) scores of 86.96% and 81.91% are obtained for these two datasets. 

EGDE-Net: A building change detection method for high-resolution remote sensing imagery based on edge guidance and differential enhancement

MFVNet: a deep adaptive fusion network with multiple field-of-views for remote sensing image semantic segmentation

Due to the abundant features of high spatial resolution (HSR) remote sensing images, change detection of these images is crucial to understanding the land-use and land-cover (LULC) changes. However, previous works mostly focus on traditional binary change detection without considering the semantic information of the change classes. The latest progress of deep learning (DL) shows its advantages in HSR remote sensing images change detection. However, due to the large number of parameter calculations, the DL network always requires a large quantity of labeled data. In addition, DL methods for change detection usually follow a patch-based learning framework, which considers only the local area and leads to a sample imbalance problem for semantic change detection. To address the above issues, we first proposed a Siamese global learning (Siam-GL) framework, which is a novel semantic change detction framework for HSR remote sensing images. In Siam-GL, the Siamese architecture with shared parameters is constructed to effectively extract the representative features of bi-temporal HSR remote sensing images. The global hierarchical (G-H) sampling mechanism is designed to address the imbalanced training sample problem with insufficient samples. Furthermore, the binary change mask is added between the encoder and decoder to weaken the influence of the no-change regional background on the change regional foreground, further improving the accuracy of the proposed framework. The experimental results obtained with three diverse HSR datasets of typical Chinese cities demonstrated that the Siam-GL framework outperforms the advanced semantic change detection methods in terms of both quantity and quality. Moreover, to verify the generalization performance of the Siam-GL framework, a larger dataset was used for evaluation, and the results show that the Siam-GL framework has strong generalization performance. 

Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery

Massive traffic scene data for algorithm research and model training is the fundamental for self-driving car technology development. In the procedure of scene image labeling, the most accurate method is manual annotation, but with the increasing of the amount of image data, artificial annotation method becomes infeasible due to its disadvantages of vast cost, inefficiency and subjective deviation. In order to reduce the time and cost of annotation processing while ensuring the accuracy, this paper proposed a semi-automatic annotation procedure. The core idea is using an automatic preprocessing method developed with convolution neural network to roughly annotate the image, just before the human review and revision. The innovation of the proposed automatic annotation method includes: 1.) the results of CNN processing are improved by combining with the object detection outcome; 2.) a variable parameter outlier-merging algorithm based on the sliding window is provided to deal with the large number of outliers. It shows about 5 percentage improvement in class average accuracy and 4/5 decrease of time to process one picture by using our processing method.

A Semi-Automatic Annotation Technology for Traffic Scene Image Labeling Based on Deep Learning Preprocessing

Using deep learning for visual scene parsing will satisfy the demand of the next generation of automatic driving technology. However, current parsing algorithms are not mature enough for practical applications unless higher accuracy and efficiency are obtained. We propose a novel scene parsing algorithm framework which integrates the object detection technologies into convolution neural network to improve the overall effectiveness. The framework consists of three components: i) a scene parsing network presenting primary semantic segmentation result. ii)an object detection network calculating the location and confidence of the targets in images. iii) an integration and filter module that cascades previous two results. Extensive experiments suggest that our model is capable of practical use and achieving more favorable scene parsing performance of mIoU score as 69.4% on CamVid dataset.

Research on Scene Parsing Algorithm Cascading Object Detection Network

Forest fires are one of the most widespread, frequent and harmful disasters. Accurate information over burned areas (BA) is essential for the assessment and restoration of economic and ecological losses. However, the feature representation of BA is often complicated. Meanwhile, since the spectral similarity among land cover categories, changes of background information have a greater impact. In addition, there are very few publicly available forest fire datasets. To solve the above problems, an unsupervised knowledge-guided symmetric deep network (UKSD-Net) is proposed for BA detection, where multi-index combination is utilized to incorporate the prior knowledge. The background changes are suppressed with the combination of symmetric deep network and Slow Feature Analysis (SFA). More reliable pseudo samples generated by uncertainty analysis are used to improve the final performance. Besides, we construct two forest fire datasets. The experimental results on the proposed dataset show that the framework is able to extract accurate and complete BA of multi-temporal remote sensing imagery.

Uksd-Net: an Unsupervised Knowledge-Guided Symmetric Deep Network for Forest Burned Areas Detection

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