Search

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: [ABSTRACT FROM AUTHOR]

High spatial resolution (HSR) remote-sensing image scene classification is a crucial way for land-use interpretation. However, most of the current scene classification methods assume that the training and test sets of remote-sensing images follow the same feature distribution. In practical application, this assumption is difficult to guarantee. Domain adaptation (DA) is a machine-learning paradigm that can effectively alleviate such problems. However, previous studies mostly focused on aligning the global distribution of the source domain (SD) and the target domain (TD), which loses the inter-subdomain contextual relations between both domains and ignores the redundancy among features. However, most DA methods usually only use the manually designed measurement criteria to establish the relationship between the SD and the TD, which is insufficient or complicated. In this article, a weakly pseudo-supervised decorrelated subdomain adaptation (WPS-DSA) framework is proposed for HSR cross-domain land-use classification. In WPS-DSA, a feature extractor based on the subdomain adaptation network is used to extract the inter-subdomain characteristics of both domains. To weaken the influence of the features redundancy among remote-sensing images, the switchable whitening (SW) module is introduced. In addition, a domain hierarchical sampling (DHS) mechanism is designed to strengthen the connection between SD and the TD simply. Moreover, the WuHan-ShangHai (WH-SH) dataset that is sampled from two typical Chinese cities is constructed to verify the generalization of the proposed framework. The experimental results of the cross-domain tasks on three publicly available HSR datasets and the WH-SH DA dataset display considerable performance and generalization ability of WPS-DSA. [ABSTRACT FROM AUTHOR]

Shadow detection automatically marks shadow pixels in high-spatial-resolution (HSR) imagery with specific categories based on meaningful colorific features. Accurate shadow mapping is crucial in interpreting images and recovering radiometric information. Recent studies have demonstrated the superiority of deep learning in very-high-resolution satellite imagery shadow detection. Previous methods usually overlap convolutional layers but cause the loss of spatial information. In addition, the scale and shape of shadows vary, and the small and irregular shadows are challenging to detect. In addition, the unbalanced distribution of the foreground and the background causes the common binary cross-entropy loss function to be biased, which seriously affects model training. A contextual detail-aware network (CDANet), a novel framework for extracting accurate and complete shadows, is proposed for shadow detection to remedy these issues. In CDANet, a double branch module is embedded in the encoder–decoder structure to effectively alleviate low-level local information loss during convolution. The contextual semantic fusion connection with the residual dilation module is proposed to provide multiscale contextual information of diverse shadows. A hybrid loss function is designed to retain the detailed information of the tiny shadows, which per-pixel calculates the distribution of shadows and improves the robustness of the model. The performance of the proposed method is validated on two distinct shadow detection datasets, and the proposed CDANet reveals higher portability and robustness than other methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High spatial resolution (HSR) imagery scene classification, which involves labeling an HSR image with a specific semantic class according to the geographical properties, has received increased attention, and many algorithms have been proposed for this task. The employment of the probabilistic topic model to acquire latent topics and the convolutional neural networks (CNNs) to capture deep features for representing HSR images has been an effective ways to bridge the semantic gap. However, the midlevel topic features are usually local and significant, whereas the high-level deep features convey more global and detailed information. In this paper, to discover more discriminative semantics for HSR images, the adaptive deep sparse semantic modeling (ADSSM) framework combining sparse topics and deep features is proposed for HSR image scene classification. In ADSSM, the fully sparse topic model and a CNN are integrated. To exploit the multilevel semantics for HSR scenes, the sparse topic features and deep features are effectively fused at the semantic level. Based on the difference between the sparse topic features and the deep features, an adaptive feature normalization strategy is proposed to improve the fusion of the different features. The experimental results obtained with four HSR image classification data sets confirm that the proposed method significantly improves the performance when compared with the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

High spatial resolution (HSR) imagery scene classification has been the subject of increased interest in recent years, and has great potential for many applications, such as urban functional analysis. Rooted in natural information processing, the use of the probabilistic topic model (PTM) to capture latent topics to represent HSR images has been an effective way to bridge the semantic gap. However, how to effectively discover discriminative information to recognize the HSR scenes is a challenging task. In this paper, the sparse homogeneous–heterogeneous topic feature model (SHHTFM) is proposed for HSR image scene classification. Differing from the conventional PTM-based scene classification methods, which utilize only heterogeneous features, SHHTFM explores the effect of the homogeneous information. Based on the union of uniform grid sampling and simple linear iterative clustering superpixel sampling, SHHTFM exploits both the heterogeneous and homogeneous information. After separately mining different types of low-level features and latent topics, the sparse topic inference procedure of SHHTFM further improves the fusion of the sparse heterogeneous and homogeneous topics. In addition, multisource geographical data are effectively integrated, where the water and vegetation boundaries define a more accurate way to restrict the boundaries of different scenes, and are then combined with the road network data to further improve the scene annotation performance. This provides more reliable and applicable results for us to better understand the complex scenes. The experimental results obtained with two HSR image classification data sets and an HSR image annotation data set demonstrate that the proposed SHHTFM framework can solve the scene classification problem, with a high classification accuracy as well as a high time efficiency. [ABSTRACT FROM AUTHOR]

In high spatial resolution (HSR) imagery scene classification, it is a challenging task to recognize the high-level semantics from a large volume of complex HSR images. The probabilistic topic model (PTM), which focuses on modeling topics, has been proposed to bridge the so-called semantic gap. Conventional PTMs usually model the images with a dense semantic representation and, in general, one topic space is generated for all the different features. However, this approach fails to consider the sparsity of the semantic representation, the classification quality, as well as the time consumption. In this paper, to solve the above problems, a fully sparse semantic topic model (FSSTM) framework is proposed for HSR imagery scene classification. FSSTM, with an elaborately designed modeling procedure, is able to represent the image with sparse but representative semantics. Based on this framework, the topic weights of multiple features are exploited by solving a concave maximization problem, which improves the fusion of the discriminative semantic information at the topic level. Meanwhile, the sparsity and representativeness of the topics generated by FSSTM guarantee that the image is adaptive to the change of a topic number. FSSTM can consistently achieve a good performance with a limited number of training samples, and is robust for HSR image scene classification. The experimental results obtained with three different types of HSR image data sets confirm that the proposed algorithm is effective in improving the performance of scene classification, and is highly efficient in discovering the semantics of HSR images when compared with the state-of-the-art PTM methods. [ABSTRACT FROM PUBLISHER]

Scene classification has been studied to allow us to semantically interpret high spatial resolution (HSR) remote sensing imagery. The bag-of-visual-words (BOVW) model is an effective method for HSR image scene classification. However, the traditional BOVW model only captures the local patterns of images by utilizing local features. In this letter, a local–global feature bag-of-visual-words scene classifier (LGFBOVW) is proposed for HSR imagery. In LGFBOVW, the shape-based invariant texture index is designed as the global texture feature, the mean and standard deviation values are employed as the local spectral feature, and the dense scale-invariant feature transform (SIFT) feature is employed as the structural feature. The LGFBOVW can effectively combine the local and global features by an appropriate feature fusion strategy at histogram level. Experimental results on UC Merced and Google data sets of SIRI-WHU demonstrate that the proposed method outperforms the state-of-the-art scene classification methods for HSR imagery. [ABSTRACT FROM PUBLISHER]

Scene classification has been proved to be an effective method for high spatial resolution (HSR) remote sensing image semantic interpretation. The probabilistic topic model (PTM) has been successfully applied to natural scenes by utilizing a single feature (e.g., the spectral feature); however, it is inadequate for HSR images due to the complex structure of the land-cover classes. Although several studies have investigated techniques that combine multiple features, the different features are usually quantized after simple concatenation (CAT-PTM). Unfortunately, due to the inadequate fusion capacity of \boldsymbolk-means clustering, the words of the visual dictionary obtained by CAT-PTM are highly correlated. In this paper, a semantic allocation level (SAL) multifeature fusion strategy based on PTM, namely, SAL-PTM (SAL-pLSA and SAL-LDA) for HSR imagery is proposed. In SAL-PTM: 1) the complementary spectral, texture, and scale-invariant-feature-transform features are effectively combined; 2) the three features are extracted and quantized separately by \boldsymbolk-means clustering, which can provide appropriate low-level feature descriptions for the semantic representations; and 3)the latent semantic allocations of the three features are captured separately by PTM, which follows the core idea of PTM-based scene classification. The probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA) models were compared to test the effect of different PTMs for HSR imagery. A U.S. Geological Survey data set and the UC Merced data set were utilized to evaluate SAL-PTM in comparison with the conventional methods. The experimental results confirmed that SAL-PTM is superior to the single-feature methods and CAT-PTM in the scene classification of HSR imagery. [ABSTRACT FROM PUBLISHER]

By labelling high spatial resolution (HSR) images with specific semantic classes according to geographical properties, scene classification has been proven to be an effective method for HSR remote sensing image semantic interpretation. Deep learning is widely applied in HSR remote sensing scene classification. Most of the scene classification methods based on deep learning assume that the training datasets and the test datasets come from the same datasets or obey similar feature distributions. However, in practical application scenarios, it is difficult to guarantee this assumption. For new datasets, it is time-consuming and labor-intensive to repeat data annotation and network design. The neural architecture search (NAS) can automate the process of redesigning the baseline network. However, traditional NAS lacks the generalization ability to different settings and tasks. In this paper, a novel neural network search architecture framework—the spatial generalization neural architecture search (SGNAS) framework—is proposed. This model applies the NAS of spatial generalization to cross-domain scene classification of HSR images to bridge the domain gap. The proposed SGNAS can automatically search the architecture suitable for HSR image scene classification and possesses network design principles similar to the manually designed networks. To obtain a simple and low-dimensional search space, the traditional NAS search space was optimized and the human-the-loop method was used. To extend the optimized search space to different tasks, the search space was generalized. The experimental results demonstrate that the network searched by the SGNAS framework with good generalization ability displays its effectiveness for cross-domain scene classification of HSR images, both in accuracy and time efficiency. [ABSTRACT FROM AUTHOR]

Scene classification is a means to interpret high-resolution remote sensing (HRS) imagery, to obtain the high-level semantic information, which can provide a reliable reference for urban planning and monitoring. The traditional scene classification methods based on HRS imagery take uniform grid cells as the scene units, thereby missing the geographic boundaries and leading to the mosaicking effect. Therefore, in this paper, the urban scene is defined as a geographic unit bordered by the road network. The task of urban scene understanding is to recognize the socioeconomic or natural semantics of the urban scene. However, due to the complexity of the urban environment, the effectiveness of the traditional scene classification methods is limited on account of three problems when applied in urban scenes: 1) The lack of socioeconomic information in HRS images, with which it is difficult to discriminate diverse urban scenes with similar exteriors. 2) The large discrepancy in the sizes and shapes of urban land parcels affects the scene feature extraction and representation. 3) Urban scene understanding frameworks that can embed various scene classification models have rarely been studied. In this paper, to solve these problems, a universal urban scene understanding framework based on multi-source geographic data (USUMG) is proposed. In the USUMG framework, road network and water channel data from OpenStreetMap (OSM) are used for generating the urban scene units. For each irregular unit, a scene decomposition method based on a morphological skeleton is employed to represent the urban scene unit by unified processing patches. To integrate the different data sources, the high-level semantic features extracted from the HRS imagery and the socioeconomic features extracted from point of interest (POI) data are fused to determine the urban scene category. Finally, the USUMG framework with various scene classification methods was tested in urban districts of Wuhan and Macao in China to verify the universality and feasibility of the proposed framework. The experimental performances are provided in this paper as a benchmark for urban scene understanding based on multi-source geographic data. [ABSTRACT FROM AUTHOR]

Reliable urban land-use maps are essential for urban analysis because the spatial distribution of land use reflects the complex environment of cities under the combined effects of nature and socio-economics. In recent years, very high resolution (VHR) remote sensing imagery interpretation has resolved the "semantic gap" between the low-level data and the high-level semantic scenes, and has been used to map urban land use. Nevertheless, the existing frameworks cannot easily be applied to practical urban analysis, which can be attributed to three main reasons: 1) the indistinguishable socio-economic attributes of the same ground object layouts; 2) the weak transferability of the supervised frameworks and the time-consuming training sample annotation; and 3) the category system inconsistency between the data source and the urban land-use application. In this paper, to achieve an "application gap" breakthrough for urban land-use mapping, a data-driven point, line, and polygon semantic object mapping (PLPSOM) framework is proposed, which makes full use of open-source VHR images and multi-source geospatial data. In the PLPSOM framework, point, line, and polygon semantic objects are represented by the points of interest (POIs), OpenStreetMap (OSM) data, and VHR images corresponding to the scenes in the land-use mapping units, respectively. OSM line semantic objects are utilized to supply the boundaries of the land-use mapping units for the POIs and VHR images, forming urban land parcels (street blocks). To reduce the cost of the data annotation, the training dataset is constructed using multiple open-source data sources. An enhanced deep adaptation network (EDAN) is then proposed to acquire the categories of the VHR scene images in the case of partial transfer learning. Finally, in order to meet the actual needs, a rule-based category mapping (RCM) model is applied to integrate the categories of the POIs and VHR images into the urban land-use category system, allowing us to acquire the land-use maps of the cities. The effectiveness of the proposed method was tested in four cities of China, including six specific areas: Beijing and Wuhan city centers; the Hanyang District of Wuhan; the Hannan District of Wuhan; Macao; and the Wan Chai area of Hong Kong, achieving a high classification accuracy. The "urban image" analysis confirmed the practicality of the obtained urban land-use maps. Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: a case study of Chinese cities • Bridging the application gap between remote sensing and land-use is important. • A point-line-polygon semantic object mapping (PLPSOM) framework is proposed. • PLPSOM uses the open-source data to reduce the time-consuming sample production. • PLPSOM represents the urban land parcel by point-line-polygon semantic object. • PLPSOM has been successfully applied in six areas of the four Chinese cities. [ABSTRACT FROM AUTHOR]