Spatial Resolution Remote Sensing Imagery Research Articles

Object Recognition of Urban Buildings in High Spatial Resolution Remote Sensing Imagery

High-resolution remote sensing images can finely express rich surface information. Using the macroscopic and spatial-temporal full coverage advantages of high-resolution remote sensing images for urban building objectification recognition is a current research hot spot in the field of remote sensing image analysis and application. However, the current research still lacks effective technical means to convert surface elements quickly and accurately from remote sensing image space to geographic information space. In this paper, the complementary advantages between image processing and deep learning are combined to research target-level extraction of high-resolution remote sensing urban buildings based on the building element information data. The experimental results achieved the precision of 0.9481 and the recall of 0.9733, indicating that the method proposed in this paper can be applied to the effective extraction of urban-level buildings, which expands the theoretical basis of the object recognition method of remote sensing thematic features based on the idea of object image analysis.

Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery

The spatial distribution pattern of jobs and housing plays a vital role in urban planning and traffic construction. However, obtaining the jobs-housing distribution at a fine scale (e.g., the perspective of individual jobs-housing attribute) presents difficulties due to a lack of social media data and useful models. With user data acquired from a location-based service provider in China, this study employs a deep bag-of-features network (BagNet) to classify remote-sensing (RS) images into various jobs-housing types. Considering Wuhan, one of the fastest developing cities in China, as a case study area, three jobs-housing types (i.e., only working, only living, and both working and living) at the land-parcel level are obtained. We demonstrate that the multiscale random sampling method can reduce the influence of image noise, increase the utilization of training data, and reduce network overfitting. By altering the network structure and the training strategy, BagNet achieved excellent fitting accuracy for identifying each jobs-housing type (overall accuracy > 0.84 and kappa > 0.8). For the first time, we demonstrate that urban socioeconomic characteristics can be obtained from high-resolution RS images using deep learning techniques. Additionally, we conclude that the total level of mixing within Wuhan is not high at present; however, Wuhan is continuously improving the mixture of jobs and housing. This study has reference value for extracting urban socioeconomic characteristics from RS images and could be used in urban planning as well as government management.

Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery.

High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic object-based image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottom-up classifications from visual features to semantic categories, but ignore top-down feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent “self-learning ability” of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Compared with traditional methods, our method is completely data-driven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multi-source and multi-temporal water bodies, and can effectively avoid confusion with shadows and other ground features.

An Object-Based Markov Random Field Model with Anisotropic Penalty for Semantic Segmentation of High Spatial Resolution Remote Sensing Imagery

The Markov random field model (MRF) has attracted a lot of attention in the field of remote sensing semantic segmentation. But, most MRF-based methods fail to capture the various interactions between different land classes by using the isotropic potential function. In order to solve such a problem, this paper proposed a new generalized probability inference with an anisotropic penalty for the object-based MRF model (OMRF-AP) that can distinguish the differences in the interactions between any two land classes. Specifically, an anisotropic penalty matrix was first developed to describe the relationships between different classes. Then, an expected value of the penalty information (EVPI) was developed in this inference criterion to integrate the anisotropic class-interaction information and the posteriori distribution information of the OMRF model. Finally, by iteratively updating the EVPI terms of different classes, segmentation results could be achieved when the iteration converged. Experiments of texture images and different remote sensing images demonstrated that our method could show a better performance than other state-of-the-art MRF-based methods, and a post-processing scheme of the OMRF-AP model was also discussed in the experiments.

Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN

Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN

Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

Detecting Building Edges from High Spatial Resolution Remote Sensing Imagery Using Richer Convolution Features Network

As the basic feature of building, building edges play an important role in many fields such as urbanization monitoring, city planning, surveying and mapping. Building edges detection from high spatial resolution remote sensing (HSRRS) imagery has always been a long-standing problem. Inspired by the recent success of deep-learning-based edge detection, a building edge detection model using a richer convolutional features (RCF) network is employed in this paper to detect building edges. Firstly, a dataset for building edges detection is constructed by the proposed most peripheral constraint conversion algorithm. Then, based on this dataset the RCF network is retrained. Finally, the edge probability map is obtained by RCF-building model, and this paper involves a geomorphological concept to refine edge probability map according to geometric morphological analysis of topographic surface. The experimental results suggest that RCF-building model can detect building edges accurately and completely, and that this model has an edge detection F-measure that is at least 5% higher than that of other three typical building extraction methods. In addition, the ablation experiment result proves that using the most peripheral constraint conversion algorithm can generate more superior dataset, and the involved refinement algorithm shows a higher F-measure and better visual effect contrasted with the non-maximal suppression algorithm.

An adaptive scale estimating method of multiscale image segmentation based on vector edge and spectral statistics information

ABSTRACTScale computation for multiscale image segmentation has become one of the key scientific problems in urgent need to be solved in the field of geographic object-based image analysis (GEOBIA). Due to the complexity of High Spatial Resolution Remote-Sensing Imagery (HSRRSI) data itself and the scale distribution differences among geographic features, it is difficult to effectively design a global scale parameter model to guide parameters setting in large scale regions and automatically produce an acceptable segmentation result simultaneously. Utilizing the vector edge and spectral statistics information, an adaptively global scale computation method named Global Scale Computation with Vector Edge (GSCVE) has been developed for multiscale segmentation, which is firstly proposed and implemented on mean-shift segmentation algorithm as an example. The highlight of the GSCVE algorithm is that it can calculate global scale parameters for multiscale image segmentation adaptively. The validity of GSCVE algorithm was verified directly by taking GeoEye and QuickBird images as segmentation experiments sample data, respectively. In addition, comparing with the renowned eCognition® multiscale segmentation algorithm, the relative advantages of GSCVE algorithm with adaptive property and the concurrence segmentation results of large and small scale geographic features are illustrated by the visual evaluation experiments simultaneously.

Unsupervised Change Detection Based on Hybrid Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery

High spatial resolution (HSR) remote sensing images provide detailed geometric information about land cover. As a result, it is possible to detect more subtle changes with the help of HSR images. However, due to the increased spatial resolution and the limited spectral information, it is difficult to identify the real changes only through the spectral feature of the image. To fully explore the spectral–spatial information and improve the change detection performance for HSR images, this paper proposes the hybrid conditional random field (HCRF) model, which combines the traditional random field method with an object-based technique. In the proposed method, the spectral discriminative information of a single pixel is extracted by the unary potential, which is modeled using a soft clustering method to make an initial separation of changed and unchanged pixels. The pairwise potential then considers the contextual information of adjacent pixels to favor spatial smoothing. An object term is also introduced in the HCRF model to keep the homogeneity of changed objects. By the use of these approaches, the oversmoothing problem of the random field-based methods and the detection error caused by the segmentation strategy in the object-based methods can be relieved. The proposed method was tested on three HSR image data sets and outperformed the compared state-of-the-art techniques.

Remote Sensing Big Data: Theory, Methods and Applications

Nowadays, our ability to acquire remote sensing data has been improved to an unprecedented level.[...]

Classification of Urban Building Type from High Spatial Resolution Remote Sensing Imagery Using Extended MRS and Soft BP Network

This study presents a new approach for classification of building type in complex urban scene. The approach consists of two parts: extended multiresolution segmentation (EMRS) and soft classification using BP network (SBP). The technology scheme is referred to here as EMRS-SBP. EMRS is used to guide the design of descriptor. A descriptor is a feature expression or a symbolized algorithm to systematically promote the expressing capability of image features. A classifier can perform far better to discern complex pattern of combining pixels working in an EMRS-based feature space constructed by a number of such descriptors. SBP serves as a classifier model to generate natural clusters of member which refers to here as both pixels and image patches. Class-mark ensured member is denoted as sure member and the rest as unsure (fuzzy) members. The latter can be relabeled through recursive defuzzifying according to the information carried by the gradually increased sure members. By using EMRS-SBP, three building types, i.e., old-fashioned courtyard dwellings, multistorey residential buildings, and high-rise buildings, can be accurately classified from high spatial resolution imagery in a feature space constructed with fifteen descriptors including nine EMRS-based ones. There is evidence that the mean overall accuracy using SBP in the EMRS-based feature space is 19.8% higher than that using the hard classification with BP network in a single resolution segmentation space and meanwhile, the mean kappa statistic value ( κ ) is 25.1% higher.

An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery

Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a significant and challenging problem when further analyzing object-related information for civil and engineering applications. However, the computational efficiency and the separate region generation and localization steps are two big obstacles for the performance improvement of the traditional convolutional neural network (CNN)-based object detection methods. Although recent object detection methods based on CNN can extract features automatically, these methods still separate the feature extraction and detection stages, resulting in high time consumption and low efficiency. As a significant influencing factor, the acquisition of a large quantity of manually annotated samples for HSR remote sensing imagery objects requires expert experience, which is expensive and unreliable. Despite the progress made in natural image object detection fields, the complex object distribution makes it difficult to directly deal with the HSR remote sensing imagery object detection task. To solve the above problems, a highly efficient and robust integrated geospatial object detection framework based on faster region-based convolutional neural network (Faster R-CNN) is proposed in this paper. The proposed method realizes the integrated procedure by sharing features between the region proposal generation stage and the object detection stage. In addition, a pre-training mechanism is utilized to improve the efficiency of the multi-class geospatial object detection by transfer learning from the natural imagery domain to the HSR remote sensing imagery domain. Extensive experiments and comprehensive evaluations on a publicly available 10-class object detection dataset were conducted to evaluate the proposed method.

Change Detection Based on a Multifeature Probabilistic Ensemble Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery

In this letter, a multifeature probabilistic ensemble conditional random field (MFPECRF) model is proposed to perform the task of change detection for high spatial resolution (HSR) remote sensing imagery. MFPECRF not only considers the spectral feature of single pixels but also the interaction between neighborhood pixels and the structural property of the ground objects in HSR imagery to give a higher detection accuracy than the traditional random field methods, which only utilize spectral and label information. In the unary potential, the spectral and morphological features of the difference image are combined using a probabilistic ensemble strategy, and the pairwise potential considers the contextual information of the observed field. The parameters of MFPECRF are estimated using a piecewise strategy, and the final result is obtained by the use of the loopy belief propagation algorithm. The experimental results of two groups of HSR multispectral images confirm the potential of the proposed method in improving the detection accuracy for HSR imagery.

Multiscale and Multifeature Normalized Cut Segmentation for High Spatial Resolution Remote Sensing Imagery

In this paper, a framework for multiscale and multifeature normalized cut (MMNCut) segmentation is proposed for high spatial resolution (HSR) remote sensing images. Normalized cuts (NCuts), as a widely used segmentation method for natural images, can obtain a globally optimized segmentation result corresponding to the optimized partitions of a graph. However, it is difficult to apply the traditional NCuts directly to HSR images because of the huge computational complexity and the diversity of the characteristics of the land covers. In order to solve these problems, the proposed MMNCuts builds a multiscale graph based on superpixels, which can provide powerful grouping cues to guide the segmentation. Generated by different algorithms with varying parameters, superpixels can capture diverse and multiscale visual patterns of HSR images. In addition, the newly constructed graph integrates the multiscale information by considering various connection relationships. Meanwhile, the successful integration of the multifeature cues, including the spectral information, texture information, and structure information, from a large number of superpixels, helps to enhance the expression ability of the graph. Computationally, this leads to a much more efficient algorithm than the traditional NCuts, and in effect, the proposed method achieves a significantly better performance than the traditional approaches. The experimental results with three HSR image data sets demonstrate that the proposed MMNCut algorithm shows a competitive performance in both qualitative and quantitative evaluations when compared with the other state-of-the-art segmentation algorithms for HSR images.

Comprehensive urban area extraction from multispectral medium spatial resolution remote-sensing imagery based on a novel structural feature

ABSTRACTOne of the most important applications of remote sensing is urban area analysis in multispectral images. This article addresses a comprehensive method for urban area extraction in the mentioned images using a combination of classic spectral features and a new structural feature. The spectral features are used for eliminating the non-urban land covers including vegetation, water, shadows, and bright soil. Then the proposed structural feature based on the density of the spectral gradient is utilized for the final separation of urban points (UPs) from non-urban points (NUPs). The proposed method is insensitive to the spectral variation of urban areas caused by variant geographical conditions and coordinates. The proposed approach is applied to a wide variety of geographical areas from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) multispectral data including arid and desert land, mountainous land, and plain area. Furthermore, a reference land-cover map from National Land Cover Data 2001 of USA is used as the ground reference for the accuracy assessment of the proposed method. Results analysis shows better performance of the proposed method in all study sites compared with the other spectral indices and the other methods. In addition, the proposed structural feature has shown promising results compared with the spectral indices when used in its own.

Bag-of-Visual-Words Scene Classifier With Local and Global Features for High Spatial Resolution Remote Sensing Imagery

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.

Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery

Due to the complex arrangements of the ground objects in high spatial resolution (HSR) imagery scenes, HSR imagery scene classification is a challenging task, which is aimed at bridging the semantic gap between the low-level features and the high-level semantic concepts. A combination of multiple complementary features for HSR imagery scene classification is considered a potential way to improve the performance. However, the different types of features have different characteristics, and how to fuse the different types of features is a classic problem. In this paper, a Dirichlet-derived multiple topic model (DMTM) is proposed to fuse heterogeneous features at a topic level for HSR imagery scene classification. An efficient algorithm based on a variational expectation–maximization framework is developed to infer the DMTM and estimate the parameters of the DMTM. The proposed DMTM scene classification method is able to incorporate different types of features with different characteristics, no matter whether these features are local or global, discrete or continuous. Meanwhile, the proposed DMTM can also reduce the dimension of the features representing the HSR images. In our experiments, three types of heterogeneous features, i.e., the local spectral feature, the local structural feature, and the global textural feature, were employed. The experimental results with three different HSR imagery data sets show that the three types of features are complementary. In addition, the proposed DMTM is able to reduce the dimension of the features representing the HSR images, to fuse the different types of features efficiently, and to improve the performance of the scene classification over that of other scene classification algorithms based on spatial pyramid matching, probabilistic latent semantic analysis, and latent Dirichlet allocation.

Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery

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 $\boldsymbol{k}$ -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 $\boldsymbol{k}$ -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.

Detail-Preserving Smoothing Classifier Based on Conditional Random Fields for High Spatial Resolution Remote Sensing Imagery

In the field of high spatial resolution (HSR) remote sensing imagery classification, object-oriented classification and conditional random field (CRF) approaches are widely used due to their ability to incorporate the spatial contextual information. However, the selection of the optimal segmentation scale in object-oriented classification is not an easy task, and some pairwise CRF models always show an oversmooth performance. In this paper, a detail-preserving smoothing classifier based on conditional random fields (DPSCRF) for HSR imagery is proposed to apply the object-oriented strategy in the CRF classification framework, thus integrating the merits of both approaches to consider the spatial contextual information and preserve the detail information in the classification. The DPSCRF model defines suitable potential functions based on the CRF model for HSR image classification, which comprise the spatial smoothing and local class label cost terms. Both terms favor spatial smoothing in a local neighborhood to consider the spatial information. In addition, the local class label cost also considers the different label information of neighboring pixels at each iterative step in the classification to preserve the detail information. In order to deal with the spectral variability of HSR imagery, a segmentation prior is used by the object-oriented processing strategy. This models the probability of each pixel based on the segmentation regions obtained by the connected-component labeling algorithm. The experimental results with three HSR images demonstrate that the proposed classification algorithm shows a competitive performance in both the quantitative and the qualitative evaluation when compared to the other state-of-the-art classification algorithms.

Change Detection Based on Pulse-Coupled Neural Networks and the NMI Feature for High Spatial Resolution Remote Sensing Imagery

In this letter, a change detection algorithm based on pulse-coupled neural networks (PCNN) and the normalized moment of inertia (NMI) feature is proposed for high spatial resolution (HSR) remote sensing imagery. To better analyze a large remote sensing image, the whole image is divided into blocks by the use of a deblocking mechanism. The PCNN model is utilized to obtain the initial binary image, and the NMI feature is calculated based on the binary image to detect the hot spot changed areas. Finally, the changed areas are processed by expectation–maximization to obtain the final change map. The experimental results using QuickBird and IKONOS images demonstrate that the proposed algorithm has the ability to provide better change detection results for HSR images than the traditional PCNN change detection algorithms.