QuickBird Data Research Articles

UPGAN: An Unsupervised Generative Adversarial Network Based on U-Shaped Structure for Pansharpening

Pansharpening is the fusion of panchromatic images and multispectral images to obtain images with high spatial resolution and high spectral resolution, which have a wide range of applications. At present, methods based on deep learning can fit the nonlinear features of images and achieve excellent image quality; however, the images generated with supervised learning approaches lack real-world applicability. Therefore, in this study, we propose an unsupervised pansharpening method based on a generative adversarial network. Considering the fine tubular structures in remote sensing images, a dense connection attention module is designed based on dynamic snake convolution to recover the details of spatial information. In the stage of image fusion, the fusion of features in groups is applied through the cross-scale attention fusion module. Moreover, skip layers are implemented at different scales to integrate significant information, thus improving the objective index values and visual appearance. The loss function contains four constraints, allowing the model to be effectively trained without reference images. The experimental results demonstrate that the proposed method outperforms other widely accepted state-of-the-art methods on the QuickBird and WorldView2 data sets.

A Framework for Improving Urban Land Cover Using Object and Pixel-Based Techniques via Remotely Sensed Data

Recently, the advancement of remote sensing technology played a key role in urban land/cover mapping, planning, tourism, and environmental management. Images with a high spatial resolution for urban classification are widely used. Despite the high spectral resolution of the image, spectral confusion happens among different land covers. Furthermore, the shadow problem also causes poor results in the classification based on traditional per-pixel spectral approaches. This study looks at ways of improving the classification of urban land cover using QuickBird images. Maximum likelihood (ML) pixel-based supervised as well as Rule-based object-based approaches were examined on high-resolution QuickBird satellite images in Karbala City, Iraq. This study indicates that the use of textural attributes during the rule-based classification procedure can significantly improve land-use classification performance. Furthermore, the results show that rule-based results are highly effective in improving classification accuracy than pixel-based. The results of this study provide further clarity and insight into the implementation of using the object-based approach with various classifiers for the extended study. In addition, the finding demonstrated the integration of high-resolution QuickBird data and a set of attributes derived from the visible bands and geometric rule set resulted in superior class separability, thus higher classification accuracies in mapping complex urban environments.

Geoenvironmental mapping of the Mamanguape River estuary (PB)

Estuaries and mangroves are important breeding and feeding areas for many species of larval fish and invertebrates that usually complete their life cycles in the sea. The Mamanguape River estuary is located between parallels 06 ° 48' and06 ° 51 'south latitude, and meridians 35 ° 07' and 34 ° 54 'west longitude, it is configured as an Environmental Preservation Area (APA in Portuguese), which has the largest mangrove reserve in the state of Paraíba. Considering its importance in environmental and economic, it takes planning and management for the development of economic activities. Thus this paper aims to Thematic Mapping in the scale of 1:10000, identifying the environments and their Geoenvironmental Units, representing them individually on maps; moreover, treat their potential, suggesting skills for use and occupation. Digital image processing of QuickBird data and WordView, field trips, vectorization of geoenvironmental units and elements of interest, were used as methodology through the development of this paper. As result, the structuring of an Environmental Georeferenced Database of multisource Data mapping for storage and their further manipulation

A Two-Stage Pansharpening Method for the Fusion of Remote-Sensing Images

The pansharpening (PS) of remote-sensing images aims to fuse a high-resolution panchromatic image with several low-resolution multispectral images for obtaining a high-resolution multispectral image. In this work, a two-stage PS model is proposed by integrating the ideas of component replacement and the variational method. The global sparse gradient of the panchromatic image is extracted by variational method, and the weight function is constructed by combining the gradient of multispectral image in which the global sparse gradient can provide more robust gradient information. Furthermore, we refine the results in order to reduce spatial and spectral distortions. Experimental results show that our method had high generalization ability for QuickBird, Gaofen-1, and WorldView-4 satellite data. Experimental results evaluated by seven metrics demonstrate that the proposed two-stage method enhanced spatial details subjective visual effects better than other state-of-the-art methods do. At the same time, in the process of quantitative evaluation, the method in this paper had high improvement compared with that other methods, and some of them can reach a maximal improvement of 60%.

An Efficient Detail Extraction Algorithm for Improving Haze-Corrected CS Pansharpening

In order to overcome the potential distortion problem, an improved haze-corrected version of multiplicative-based component substitution pansharpening method is proposed. Our improvement is to acquire the optimal spatial detail maps in detail extraction procedure with sparse structural manifold embedding (SSME) and guided filter. First, a major spatial detail image is estimated by using SSME to well preserve the structural details, such as textures, edges, and contours. Second, a detail map is obtained by using the guided filter compensates for the local information loss of the major detail image due to inaccurate recovery for the patches with weaken structural information. Finally, the pansharpened image is acquired by proportionally injecting two detail images into the low-resolution multispectral image. The performance evaluations on degraded and real QuickBird data sets by using quality assessment metrics and visual analysis demonstrate that the proposed method achieves better results as compared with other existing methods.

Estimating tree aboveground biomass using multispectral satellite-based data in Mediterranean agroforestry system using random forest algorithm

Forest aboveground biomass (AGB) is a key biophysical variable to assess and monitor the spatio-temporal changes of forest ecosystems. AGB should be accurately and timely estimated through remote sensing to provide valuable information to better support sustainable forest management strategies. QuickBird and WorldView-2 satellites data and Random Forest (RF) regression model were used to estimate tree AGB in Mediterranean agroforestry systems. Spectral bands, vegetation indices and Grey-Level Co-occurrence Matrix (GLCM) texture features of 140 plots with and without vegetation mask were used as independent variables, while total of AGB per plot was used as dependent variable. A model with good performance was obtained for a complex agroforestry system, with an R2 of 82.0% and RMSE of 10.5 t/ha (22.6%). The top 11 most important variables have 80.3% of total relative importance, with 59.6% of GLCM textural features, 12.3% of vegetation indices and 8.4% of spectral bands. The results highlight the importance of the variable GLCM texture, and the use of vegetation mask and RF regression model to collect accurate spatial information on key crown cover attributes, by excluding the spectral contribution of understory vegetation and soil characteristic, of Mediterranean agroforestry systems.

Multibranch Feature Extraction and Feature Multiplexing Network for Pansharpening

With the continuous development of deep neural networks in the visual field, their application to panchromatic sharpening has received increasing attention from researchers; however, the existing panchromatic sharpening methods generally lack the ability to combine knowledge of the panchromatic sharpening field for feature extraction with neural networks, which have certain limitations in feature extraction and the discovery of new features. This article proposes a simple, modular, multibranched feature extraction and reuses network architecture designed not only to support feature reuse but to learn well-expressed new features for use in panchromatic sharpening approaches. In addition, we fused field knowledge of panchromatic sharpening to extract spatial structure information of panchromatic maps through gradient calculators and design structural and spectral compensation to fully extract and preserve the spatial structural and spectral information of images. We conducted experiments on the QuickBird and WorldView-3 satellite data sets, and the experimental results reveal that our proposed method has advantages over the best methods currently available, achieving excellent results not only on objective evaluation metrics, such as full-reference and no-reference metrics, but also on subjective visual evaluation.

NLRNet: An Efficient Nonlocal Attention ResNet for Pansharpening

Remote sensing images often contain many similar components, such as buildings, roads, and water surfaces, which have similar spectra and spatial structures. Although convolutional neural networks (CNNs) based on residual learning can provide excellent performance in pansharpening, the existing methods do not make full use of intrinsically similar information in images. Moreover, since the convolution operation is focused on the local region, even in a deep network, position-independent global information is difficult to obtain. In this article, an efficient nonlocal attention residual network (NLRNet) is proposed to capture the similar contextual dependencies of all pixels. Specifically, to reduce the difficulty of network training caused by the original nonlocal attention, we propose an efficient nonlocal attention (ENLA) mechanism and employ residual with zero initialization (ReZero) technology to make the signal easy to spread through the network. Furthermore, a spectral aggregation module (SpecAM) is proposed to generate fused images and adjust the corresponding spectral information. The experimental results for the QuickBird and WorldView3 data sets show that the proposed method is competitive with other advanced methods based on quality assessment and visual perception.

Weighted manifold regularized sparse representation of featured injected details for pansharpening

ABSTRACT Sparse representation (SR)-based pansharpening methods which combine the dictionary and estimated sparse coefficients have achieved visually and quantitatively great results in pansharpening problem these days. And the details injection (ID)-based methods can receive comparable images by sharpening the multispectral bands through adding the proper spatial details from panchromatic (PAN) images. Recently, method based on sparse representation of injected details (SR-D) which combines the SR and ID points out a new way forward for pansharpening. In this direction, manifold regularized sparse representation of injected details (MR-SR-D) which introducing a manifold regularization (MR) into the former SR-D model have improved the quality of pansharpened images greatly utilizing a graph Laplacian to incorporate the locally geometrical structure of the multispectral data. However, due to the lack of spatial information in PAN, and the use of higher-order features similarity with the original multispectral images, the resulting images still have spatial and spectral distortion. Thus, in this paper, we propose a new method to enhance the spatial resolution of aiming image by adding the weighted local geometrical structure of PAN and multispectral images, and improve the spectral resolution by joining the higher-order structure connection between multispectral and aiming images to the MR-SR-D method which can be called as weighted manifold regularized (WMR) sparse representation of featured injected details method (WMR-SR-FD). Experimental results using IKONOS, QuickBird and WorldView2 data show that the proposed method can achieve remarkable spectral and spatial quality.

Super-Resolution-Guided Progressive Pansharpening Based on a Deep Convolutional Neural Network

Pansharpening and super-resolution (SR) methods share the same target to improve the spatial resolution of images. Based on this similarity, we propose and develop a novel pansharpening algorithm that is guided by a deep SR convolutional neural network. The proposed framework comprises three components: an SR process, a progressive pansharpening process, and a high-pass residual module. Specifically, the SR process extracts inner spatial detail that is present in multispectral images. Then, progressive pansharpening is used as a detailed pansharpening process, and the high-pass residual module helps by directly injecting spatial detail from panchromatic images. The performance of the proposed network has been compared with that of traditional and other deep-learning-based pansharpening algorithms based on QuickBird, WorldView-3, and Landsat-8 data, and the results demonstrate the superiority of our algorithm.

OBJECT-BASED IMAGE ANALYSIS OF DIFFERENT SPATIAL RESOLUTION SATELLITE IMAGERIES IN URBAN AND SUBURBAN ENVIRONMENT

Abstract. Monitoring urban and suburban land cover has become a particularly researched investigation field in remote sensing community, since there is a large amount of professionals interested in gathering useful information, regarding urban sprawl and its side effects in natural vegetation, urban parks and water bodies. This paper focuses on studying the implementation of an object-based image analysis methodological framework, in Orfeo ToolBox. Moderate, high and very high spatial resolution satellite images were utilized in order to generate thematic land cover maps of the study area located in Thessaloniki, Greece. Taking into consideration that there is not a relevant research in literature concerning the selection of segmentation parameters values, the optimal values are presented for MeanShift segmentation algorithm. Classifications were conducted with the use of Support Vector Machines algorithm and the final outputs are presented, accompanied by the evaluation of accuracy assessments which is a mandatory step in every remote sensing project. The analysis showed that OBIA, in this case, works well with Landsat-8 and QuickBird data and exceptionally well with Sentinel-2A data with over 90% overall accuracy. Critical considerations on the aforementioned are also included.

Pan-Sharpening via Multiscale Dynamic Convolutional Neural Network

Pan-sharpening is an effective method to obtain high-resolution multispectral images by fusing panchromatic (PAN) images with fine spatial structure and low-resolution multispectral images with rich spectral information. In this article, a multiscale pan-sharpening method based on dynamic convolutional neural network is proposed. The filters in dynamic convolution are generated dynamically and locally by the filter generation network which is different from the standard convolution and strengthens the adaptivity of the network. The dynamic filters are adaptively changed according to the input images. The proposed multiscale dynamic convolutions extract detail feature of PAN image at different scales. Multiscale network structure is beneficial to obtain effective detail features. The weights obtained by the weight generation network are used to adjust the relationship among the detail features in each scale. The GeoEye-1, QuickBird, and WorldView-3 data are used to evaluate the performance of the proposed method. Compared with the widely used state-of-the-art pan-sharpening approaches, the experimental results demonstrate the superiority of the proposed method in terms of both objective quality indexes and visual performance.

Prediction and location suitability of settlement growth at The BWK II of Semarang City

Population growth in urban areas leads to the increasing demand for settlement areas and the expansion of settlement growth in inappropriate locations. Semarang is one of the important metropolitan cities in Indonesia which has problems with the supply of settlement areas. The main focus of this research is to understand the problem, including the aspects of settlement growth, forecasting, and proposing recommendations for expansion of the settlement areas. The study area is BWK II of Semarang City which consists of two Districts, Candisari and Gajahmungkur. The study area is a very potential area for settlement growth. The aim of the research was to predict the settlement growth and assess the location suitability of settlement growth based on the Master Plan document of Semarang City. The primary data for the study is obtained from QuickBird data image, and the spatial analysis was conducted by utilizing Remote Sensing, Geographic Information System, and LanduseSim as the software for prediction simulation. Accessibility and existing settlement location are considered as the driving factor while the river is considered as the constraint factor. The result of the study shows that the prediction of settlement growth is mostly consistent with the Master Plan of Semarang City. The study provides evidence that settlement growth was also observed in the conservation area and flood-prone area which are restricted for further development. The findings are beneficial for planners to reformulate the policies and strategies for future settlement growth.

Integrated Geospatial Database and Web Based Tools for Land Management and Administrative Planning (ILMAP) for Imphal East and Thoubal Districts, Manipur State, India

Manipur, as most of the north eastern area, come under hilly and difficult area, economically and infrastructurally underdeveloped area category. Manipur is facing the unenviable situation of being a resource starved State coupled with decades old insurgency widespread across the State, desperately crying out for a major thrust in inclusive development. As part of the initiative by the Govt. of India for the development of the north-eastern India, an Integrated Land Management and Administrative Planning (ILMAP) was taken up in Manipur State with the state-of-the-art technology. In the present work Imphal East and Thoubal Districts are selected. The objective of the study is to generate GIS database on drainage, water bodies, administrative boundaries, and other utility maps. The methodology involves geo-referencing of Quick Bird (QB) data with the help of DGPS survey. Drainage and surface water bodies, base map and land use/land cover maps are generated with the help of QB data. Point of Interest is mapped in all urban areas covering important landmarks such as Govt. offices, post offices, banks, religious places, and places of tourist interest etc., along with photographs and geographical coordinates. Using the soil maps generated by AISLUS and NBSS and LUP as a base, soil maps are prepared. All these maps are geo-referenced to QB satellite data. Using this database, a customized GIS application is developed for e-governance which can be accessed by authorized beneficiaries through State Wide Area Network (SWAN). The application was deployed in the State Data Centre (SDC) and training was provided to all the department officials for effective use in all developmental projects.

Pansharpening through an improved sparsity-based fusion method

ABSTRACTIn this paper, a patch-wise manner based on the sparsity is proposed to fuse a panchromatic (PAN) image and a low resolution multispectral (LMS) image. In the sparsity-based pansharpening methods, improving the training process of the dictionaries and sparse coefficients of the fused image, which is the main goal of this paper, have a significant impact on the fused results. In this paper, the fused image is obtained by minimizing the cost function which is obtained from incorporating a Markov random field (MRF)-based prior model into the maximum a posteriori (MAP) estimation. The contribution of this paper is twofold derived from our proposed prior model. 1) The prior model only involves the parts of the PAN information related to a considered band of the high-resolution multispectral image in the training process of the dictionary of the considered band. Not only does it improve the training of the dictionaries, but it also leads to finding more accurate sparse coefficients. 2) The high-frequency information of the PAN image is also involved in the training process as a separate term. This term decreases the spectral distortion by relieving the adverse effect of dissimilarity of the grey levels between the PAN and multispectral images on the fused image. The visual and quantitative comparison between the performance of the proposed method and eight well-known fusion methods on the Pleiades, QuickBird, and DEIMOS-2 data demonstrate the superiority of the proposed method.

Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme

Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

Zonation of Landslide Susceptibility and Risk Assessment in Serchhip town, Mizoram

A landslide is the downward-slope movement of soil, rock or organic material under the influence of gravity. It can be caused by a variety of reasons like intense or prolonged rainfall, earthquakes, geomorphology, slope variations and human activities. Landslides include debris flow, slide, toppling or falling movements, and many landslides exhibit a combination of two or more types of movements. Mizoram is one of the most disastrous landslide-prone areas in India. The state experienced landslides yearly during monsoon season. Landslides are a natural disaster. Though landslide occurs naturally, one of the main factors contributing to landslide in city and township can be credited to human interference. The topography of Mizoram is being relatively young; landslide can be a very major economic setbacks. Mostly there are N–S-trending anticlinal strike ridges with steep slopes and narrow intervening synclinal valleys with series of parallel ridges or topographic highs. The other landforms of the state are dissected ridges with deep gorges, spurs, keels, etc. Faulting has produced steep fault scarps. Thus, Mizoram, being a hilly terrain, is extremely prone to landslides. The extent of damage caused varies considerably from place to place, which is mainly caused by human activities and by geological processes assisted by environmental processes. The study area lies in the southern part of the Aizawl district. Remote sensing and GIS techniques are used. Recent advances of satellite remote sensing technology and geographical information system techniques have provided us a more in-depth approach to study landslide phenomena. In the present study, high-resolution satellite data such as QuickBird, IRS and Cartosat-I data were utilized to map the different landslide hazard zones of Serchhip town for undertaking mitigation measures and to identify potential zones of occurrences. Satellite data are utilized for mapping and preparing landslide hazard zones. An in-depth study of slope stability within Serchhip township was also carried out, and it was found that this locality of the town has been severely affected by subsidence and landslides, endangering the lives and property of the people. It was also found that the area comprises very soft shale and interbedded, weathered sandstone and siltstone, which belongs to the Middle Bhuban Formation of Surma Group of Tertiary age. Landslide risk assessment determines the expected degree of loss to landslide and its disruption of economic activities. The correlation of landslide susceptibility with major assets in the region helps to determine the risk assessment. Risk assessment is the final goal. The main objectives are to assess the risk, vulnerability of landslides and mapping, classify the zones of landslide hazards and suggest preventive and remedial measures, methods involving extensive fieldworks and data collections, creation of thematic layers and data analysis. From the present study, it is observed that human activities paired with natural factors have made many parts of Serchhip highly prone to landslides.

Manifold regularized sparse representation of injected details for pansharpening

ABSTRACTPansharpening with sparse representation (SR) and details injection (ID) can both produce visually and quantitatively pleasing images. The former constructs pansharpened image by combining the dictionary and estimated sparse coefficients while the later by sharpening the multispectral bands through adding the proper spatial details from panchromatic (Pan) image. The combination of these two methods has been putting forward as the pansharpening method based on sparse representation of injected details (SR-D). Although SR-D has achieved better results both in visual and quantitative parts than many state-of-art methods, it ignores the intrinsic geometric structure connection between the multispectral image (MS) and the corresponding high-resolution MS image. In this paper, we propose a new pansharpening method, called manifold regularized sparse representation of injected details (MR-SR-D) by introducing a manifold regularization (MR) into the former SR-D model. The manifold regularization utilized a graph Laplacian to incorporate the locally geometrical structure of the multispectral data. Experimental results on the IKONOS, QuickBird and WorldView2 data sets show that the proposed method can achieve remarkable spectral and spatial quality on both reduced scale and full scale.

Pansharpening with a Gradient Domain GIF Based on NSST

In order to improve the fusion quality of multispectral (MS) and panchromatic (PAN) images, a pansharpening method with a gradient domain guided image filter (GIF) that is based on non-subsampled shearlet transform (NSST) is proposed. First, multi-scale decomposition of MS and PAN images is performed by NSST. Second, different fusion rules are designed for high- and low-frequency coefficients. A fusion rule that is based on morphological filter-based intensity modulation (MFIM) technology is proposed for the low-frequency coefficients, and the edge refinement is carried out based on a gradient domain GIF to obtain the fused low-frequency coefficients. For the high-frequency coefficients, a fusion rule based on an improved pulse coupled neural network (PCNN) is adopted. The gradient domain GIF optimizes the firing map of the PCNN model, and then the fusion decision map is calculated to guide the fusion of the high-frequency coefficients. Finally, the fused high- and low-frequency coefficients are reconstructed with inverse NSST to obtain the fusion image. The proposed method was tested using the WorldView-2 and QuickBird data sets; the subjective visual effects and objective evaluation demonstrate that the proposed method is superior to the state-of-the-art pansharpening methods, and it can efficiently improve the spatial quality and spectral maintenance.

Object-based image analysis approach for vessel detection on optical and radar images

Commercial satellites for Earth observation can integrate conventional positioning and tracking systems for monitoring legal and illegal activities by sea, in order to effectively detect and prevent events threatening human life and environment. This study describes an object-oriented approach to detect vessels combining high- and medium-resolution optical and radar images. Once detected, the algorithm estimates their position, length, and heading and assigns a speed range. Tests are done using WorldView-2, QuickBird, GeoEye-1, Sentinel-2A, COSMO-SkyMed, and Sentinel-1 data imaged in several test sites including China, Australia, Italy, Hong Kong, and the western Mediterranean Sea. Validation of results with data from the automatic identification system shows that the estimates for length and heading have R2 = 0.85 and R2 = 0.92, respectively. Tests for evaluating speed from Sentinel-2 time-lag image displacement show encouraging results, with 70% of estimates’ residuals within ±2 m / s. Finally, our method is compared to the state-of-the-art search for unidentified maritime object (SUMO), provided by the European Commission’s Joint Research Centre. Finally, our method is compared to the state-of-the-art SUMO. Tests with Sentinel-1 data show similar results in terms of correct detections. Nevertheless, our method returns a smaller number of false alarms compared to SUMO.