Boundary-semantic collaborative guidance network with dual-stream feedback mechanism for salient object detection in optical remote sensing imagery

With the increasing application of deep learning in various domains, salient object detection in optical remote sensing images (ORSI-SOD) has attracted significant attention. However, most existing ORSI-SOD methods predominantly rely on local information from low-level features to infer salient boundary cues and supervise them using boundary ground truth, but fail to sufficiently optimize and protect the local information, and almost all approaches ignore the potential advantages offered by the last layer of the decoder to maintain the integrity of saliency maps. To address these issues, we propose a novel method named boundary-semantic collaborative guidance network (BSCGNet) with dual-stream feedback mechanism. First, we propose a boundary protection calibration (BPC) module, which effectively reduces the loss of edge position information during forward propagation and suppresses noise in low-level features without relying on boundary ground truth. Second, based on the BPC module, a dual feature feedback complementary (DFFC) module is proposed, which aggregates boundary-semantic dual features and provides effective feedback to coordinate features across different layers, thereby enhancing cross-scale knowledge communication. Finally, to obtain more complete saliency maps, we consider the uniqueness of the last layer of the decoder for the first time and propose the adaptive feedback refinement (AFR) module, which further refines feature representation and eliminates differences between features through a unique feedback mechanism. Extensive experiments on three benchmark datasets demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent years. Codes and results have been released on GitHub: https://github.com/YUHsss/BSCGNet.Comment: Accepted by TGR

Simulating emergent urban form: desakota in China

We propose that the emergent phenomenon know as ?desakota?, the rapidurbanization of densely populated rural populations in the newlydeveloped world, particularly China, can be simulated using agent-basedmodels which combine both local and global features. We argue thatdeskota represents a surprising and unusual form of urbanization wellmatchedto processes of land development that are driven from the bottomup but moderated by the higher-level macro economy. We develop asimple logic which links local household reform to global urban reform,translating these ideas into a model structure which reflects these twoscales. Our model first determines the rate of growth of different spatialaggregates using linear statistical analysis. It then allocates this growth tothe local level using developer agents who determine the transformation ormutation of rural households to urban pursuits based on local land costs,accessibilities, and growth management practices. The model is applied todesakota development in the Suzhou region between 1990 and 2000. Weshow how the global rates of change predicted at the township level in theWuxian City region surrounding Suzhou are tempered by localtransformations of rural to urban land uses which we predict using cellularautomata rules. The model, which is implemented in the RePast 3software, is validated using a blend of data taken from remote sensing andgovernment statistical sources. It represents an example of generativesocial science that fuses plausible behavior with formalized logics matchedagainst empirical evidence, essential in showing how novel patterns ofurbanization such as desakota emerge

Web-based Implementation of Winter Maintenance Decision Support System Using GIS and Remote Sensing, May 2005

Winter maintenance, particularly snow removal and the stress of snow removal materials on public structures, is an enormous budgetary burden on municipalities and nongovernmental maintenance organizations in cold climates. Lately, geospatial technologies such as remote sensing, geographic information systems (GIS), and decision support tools are roviding a valuable tool for planning snow removal operations. A few researchers recently used geospatial technologies to develop winter maintenance tools. However, most of these winter maintenance tools, while having the potential to address some of these information needs, are not typically placed in the hands of planners and other interested stakeholders. Most tools are not constructed with a nontechnical user in mind and lack an easyto-use, easily understood interface. A major goal of this project was to implement a web-based Winter Maintenance Decision Support System (WMDSS) that enhances the capacity of stakeholders (city/county planners, resource managers, transportation personnel, citizens, and policy makers) to evaluate different procedures for managing snow removal assets optimally. This was accomplished by integrating geospatial analytical techniques (GIS and remote sensing), the existing snow removal asset management system, and webbased spatial decision support systems. The web-based system was implemented using the ESRI ArcIMS ActiveX Connector and related web technologies, such as Active Server Pages, JavaScript, HTML, and XML. The expert knowledge on snow removal procedures is gathered and integrated into the system in the form of encoded business rules using Visual Rule Studio. The system developed not only manages the resources but also provides expert advice to assist complex decision making, such as routing, optimal resource allocation, and monitoring live weather information. This system was developed in collaboration with Black Hawk County, IA, the city of Columbia, MO, and the Iowa Department of transportation. This product was also demonstrated for these agencies to improve the usability and applicability of the system

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed

Multisource Data Integration in Remote Sensing

Papers presented at the workshop on Multisource Data Integration in Remote Sensing are compiled. The full text of these papers is included. New instruments and new sensors are discussed that can provide us with a large variety of new views of the real world. This huge amount of data has to be combined and integrated in a (computer-) model of this world. Multiple sources may give complimentary views of the world - consistent observations from different (and independent) data sources support each other and increase their credibility, while contradictions may be caused by noise, errors during processing, or misinterpretations, and can be identified as such. As a consequence, integration results are very reliable and represent a valid source of information for any geographical information system

Investigation related to multispectral imaging systems

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community

Development of an optical sensor for real-time weed detection using laser based spectroscopy

The management of weeds in agriculture is a time consuming and expensive activity, including in Australia where the predominant strategy is blanket spraying of herbicides. This approach wastes herbicide by applying it in areas where there are no weeds. Discrimination of different plant species can be performed based on the spectral reflectance of the leaves. This thesis describes the development of a sensor for automatic spot spraying of weeds within crop rows. The sensor records the relative intensity of reflected light in three narrow wavebands using lasers as an illumination source. A prototype weed sensor which had been previously developed was evaluated and redesigned to improve its plant discrimination performance. A line scan image sensor replacement was chosen which reduced the noise in the recorded spectral reflectance properties. The switching speed of the laser sources was increased by replacing the laser drivers. The optical properties of the light source were improved to provide a more uniform illumination across the viewing area of the sensor. A new opto-mechanical system was designed and constructed with the required robustness to operate the weed sensor in outdoor conditions. Independent operation of the sensor was made possible by the development of hardware and software for an embedded controller which operated the opto-electronic components and performed plant discrimination. The first revised prototype was capable of detecting plants at a speed of 10 km/h in outdoor conditions with the sensor attached to a quad bike. However, it was not capable of discriminating different plants. The final prototype included a line scan sensor with increased dynamic range and pixel resolution as well as improved stability of the output laser power. These changes improved the measurement of spectral reflectance properties of plants and provided reliable discrimination of three different broadleaved plants using only three narrow wavelength bands. A field trial with the final prototype demonstrated successful discrimination of these three different plants at 5 km/h when a shroud was used to block ambient light. A survey of spectral reflectance of four crops (sugarcane, cotton, wheat and sorghum) and the weeds growing amongst these crops was conducted to determine the potential for use of the prototype weed sensor to control spot-spraying of herbicides. Visible reflectance spectra were recorded from individual leaves using a fibre spectrometer throughout the growing season for each crop. A discriminant analysis was conducted based on six narrow wavebands extracted from leaf level spectral reflectance measured with a spectrometer. The analysis showed the potential to discriminate cotton and sugarcane fro

Research on enhancing the utilization of digital multispectral data and geographic information systems in global habitability studies

The University of Kansas Applied Remote Sensing (KARS) program is engaged in a continuing long term research and development effort designed to reveal and facilitate new applications of remote sensing technology for decision makers in governmental agencies and private firms. Some objectives of the program follows. The development of new modes of analyzing multispectral scanner, aerial camera, thermal scanner, and radar data, singly or in concert in order to more effectively use these systems. Merge data derived from remote sensing with data derived from conventional sources in geographic information systems to facilitate better environmental planning. Stimulation of the application of the products of remote sensing systems to problems of resource management and environmental quality now being addressed in NASA's Global Habitability directive. The application of remote sensing techniques and analysis and geographic information systems technology to the solution of significant concerns of state and local officials and private industry. The guidance, assistance and stimulation of faculty, staff and students in the utilization of information from the Earth Resources Satellite (LANDSAT) and Aircraft Programs of NASA in research, education, and public service activities carried at the University of Kansas