Narrow Results

An image clustering method that is based on the particle swarm optimizer (PSO) is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together with similar image primitives. To illustrate its wide applicability, the proposed image classifier has been applied to synthetic, MRI and satellite images. Experimental results show that the PSO image classifier performs better than state-of-the-art image classifiers (namely, K-means, Fuzzy C-means, K-Harmonic means and Genetic Algorithms) in all measured criteria. The influence of different values of PSO control parameters on performance is also illustrated.

Geologists employ high-spatial-resolution (HR) remote sensing (RS) data for many diverse applications as they effectively reflect detailed geological information, enabling high-quality and efficient geological surveys. Applications of HR RS data to geological and related fields have grown recently. HR optical remote sensing data are widely used in geological hazard assessment, seismic monitoring, mineral exploitation, glacier monitoring, and mineral information extraction due to high accuracy and clear object features. By reviewing these applications, we can better understand the results of previous studies and more effectively use the latest data and methods to efficiently extract key geological information. Compared with optical satellite images, synthetic-aperture radar (SAR) images are stereoscopic and exhibit clear relief, strong performance, and good detection of terrain, landforms, and other information. SAR images have been applied to seismic mechanism research, volcanic monitoring, topographic deformation, and fault analysis. Furthermore, a multi-standard maturity analysis of the geological applications of HR images reveals that optical remote sensing data are superior to radar data for mining, geological disaster, lithologic, and volcanic applications, but inferior for earthquake, glacial, and fault applications. Therefore, it is necessary for geological remote sensing research to be truly multi-disciplinary or inter-disciplinary, ensuring more detailed and efficient surveys through cross-linking with other disciplines. Moreover, the recent application of deep learning technology to remote sensing data extraction has improved the capabilities of automatic processing and data analysis with HR images.

Crop pests and diseases are treated as one of the main factors affecting food production and security. An accurate detection and corresponding precision management to reduce the spread of crop diseases in time and space is an important scientific issue in crop disease control tasks. On the one hand, the development of remote sensing technology provides higher-quality data (high spectral/spatial resolution) for crop disease monitoring. On the other hand, deep learning/machine learning algorithms also provide novel insights for crop disease detection. In this paper, a comprehensive review was conducted to demonstrate various remote sensing platforms (e.g. ground-based, low-attitude and spaceborne scales) and popular sensors (e.g. RGB, multispectral and hyperspectral sensors). In addition, conventional machine learning and deep learning algorithms applied for crop disease monitoring are also reviewed. In the end, considering the crop disease early detection problem which is a challenging problem in this area, self-supervised learning is introduced to motivate future research. It is envisaged that this paper has concluded the recent crop disease monitoring algorithms and provides a novel thought on crop disease early monitoring.

Machine learning (ML) approaches as part of the artificial intelligence domain are becoming increasingly important in multispectral and hyperspectral remote sensing analysis. This is due to the fact that there is a significant increase in the quality and quantity of the remote sensing sensors that produce data of higher spatial and spectral resolutions. With higher resolutions, more information can be extracted from the data, which require more complex and sophisticated techniques compared to the traditional approaches of data analysis. Machine learning approaches are able to analyse remote sensing (RS) data more effectively and give higher classification accuracy. This review will discuss and demonstrate some applications of machine learning techniques in the processing of multispectral and hyperspectral remote sensing data. Future recommendations will also be given to highlight the way forward in the use of machine learning approaches in optical remote sensing data analysis.

Current state-of-the-art detectors achieved impressive performance in detection accuracy with the use of deep learning. However, most of such detectors cannot detect objects in real time due to heavy computational cost, which limits their wide application. Although some one-stage detectors are designed to accelerate the detection speed, it is still not satisfied for task in high-resolution remote sensing images. To address this problem, a lightweight one-stage approach based on YOLOv3 is proposed in this paper, which is named Squeeze-and-Excitation YOLOv3 (SE-YOLOv3). The proposed algorithm maintains high efficiency and effectiveness simultaneously. With an aim to reduce the number of parameters and increase the ability of feature description, two customized modules, lightweight feature extraction and attention-aware feature augmentation, are embedded by utilizing global information and suppressing redundancy features, respectively. To meet the scale invariance, a spatial pyramid pooling method is used to aggregate local features. The evaluation experiments on two remote sensing image data sets, DOTA and NWPU VHR-10, reveal that the proposed approach achieves more competitive detection effect with less computational consumption.

Passive microwave remote sensing of the Earth from space provides information essential for understanding the Earth’s environment and its evolution. Parameters such as soil moisture, sea surface temperature and salinity, and profiles of atmospheric temperature and humidity are measured at frequencies determined by the physics (e.g. sensitivity to changes in desired parameters) and by the availability of suitable spectrum free from interference. Interference from man-made sources (radio frequency interference) is an impediment that in many cases limits the potential for accurate measurements from space. A review is presented here of the frequencies employed in passive microwave remote sensing of the Earth from space and of the associated experience with RFI and contemporary approaches to address the problem.

Ants, bees and other social insects deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone that causes clumping or clustering behavior in a species and brings individuals into a closer proximity is called aggregation pheromone. This article presents a novel method for change detection in remotely sensed images considering the aggregation behavior of ants. Change detection is viewed as a segmentation problem where changed and unchanged regions are segmented out via clustering. At each location of data point, representing a pixel, an ant is placed; and the ants are allowed to move in the search space to find out the points with higher pheromone density. The movement of an ant is governed by the amount of pheromone deposited at different points of the search space. More the deposited pheromone, more is the aggregation of ants. This leads to the formation of homogenous groups of data. Evaluation on two multitemporal remote sensing images establishes the effectiveness of the proposed algorithm over an existing thresholding algorithm.

In recent years, with the development of remote sensing technology and the enhancement of the value of remote sensing images in military and civil fields, remote sensing image object segmentation has also received more and more attention. This paper mainly studies the application of instance segmentation based on deep convolutional neural network in the remote sensing image. This paper proposes an attention balanced feature pyramid module, which strengthens multi-level features and uses the attention module to suppress the interference features of noise in the complex background. In addiction, Soft-NMS is introduced to improve the performance of the network, and GIoU loss is introduced to improve the effect of object detection. The proposed network improves the average detection and segmentation accuracy (mAP) values from $41.75\%$ and $35.34\%$ to $43.05\%$ and $36.02\%$, respectively.

This study assesses the short- and long-term changing position of the shorelines along the barrier lagoon and mud section of the Nigeria coast using remote sensing techniques. Five shoreline positions, covering a 30-year period (1986–2016), were extracted from medium resolution multi-spectral Landsat satellite imageries using both manual and semi-automatic shoreline extraction methods. Approximately, 533 orthogonal transects were cast using DSAS at simple right angles along the entire coast at 250-m interval. The shoreline change analysis was calculated using the Net Shoreline Movement and the End Point Rate techniques. The results show that the shoreline is highly dynamic; with the average rate of erosion estimated to be 28.08m/year and the average rate of accretion estimated to be 20.56m/year. While the persistence of erosional tendencies was found mainly in the Okesiri-Abereke-Aiyetoro parts of the mud section of the shoreline, the accretional tendencies was found mainly in the Aboraji-Araromi (barrier lagoon) and the Ajegunle-Jinriwo-Awoye (mud section) parts of the shoreline. The high dynamism of the shoreline is mainly attributed to the increasing frequency of storm surges in the area with over 13 incidents experienced within this period. This study submits that addressing coastal erosion and flooding problems in Nigeria should be based on the system boundary model where the coastal process and dynamics are constantly monitored holistically rather than locally or regionally as it is currently being done. This will also ensure the incorporation of the extent, frequency and intensity of extreme event in the development of adaptation measures.

The December 26, 2004 tsunami has caused extensive damage in the Union Territory of Andaman & Nicobar Islands, India, affecting 115.36 km of coastline. In order to identify the impacts of tsunami in South Andaman of the Andaman Islands, the study has been carried out using satellite data for pre-tsunami (Feb. 2003) and post-tsunami (March 2005). This paper provides an assessment of damages caused by tsunami and suitable resettlement places for the people using remote sensing and GIS technology. Assessment of tsunami inflicted damage to island ecosystems assumes greater importance owing to their life-sustaining and livelihood support abilities. Apart from the reparation caused to life and property, significant damage has afflicted the ecosystem, which will have long lasting effects. The tsunami-induced damage to coastal ecosystems was studied based on coastal landuse, geomorphology and coastal critical habitat for South Andaman Island using remote sensing and GIS. An area of 3,366 ha of land area was affected by tsunami. Within the coastal ecosystem, coral reef and mangrove were also severely affected. The study of landforms shows that the land is submerged. The severity of damages and their consequences suggest the need for a definite restoration ecology programme.

Manual field-based population census data collection method is slow and expensive, especially for refugee management situations where more frequent censuses are necessary. This study aims to explore the approaches of population estimation of Rohingya migrants using remote sensing and machine learning. Two different approaches of population estimation viz., (i) data-driven approach and (ii) satellite image-driven approach have been explored. A total of 11 machine learning models including Artificial Neural Network (ANN) are applied for both approaches. It is found that, in situations where the surface population distribution is unknown, a smaller satellite image grid cell length is required. For data-driven approach, ANN model is placed fourth, Linear Regression model performed the worst and Gradient Boosting model performed the best. For satellite image-driven approach, ANN model performed the best while Ada Boost model has the worst performance. Gradient Boosting model can be considered as a suitable model to be applied for both the approaches.

Remote sensing techniques have been developed over the past decades to acquire data without being in contact of the target object or data source. Their application on land-cover image segmentation has attracted significant attention during recent years. With the help of satellites, scientists and researchers can collect and store high-resolution image data that can be further be processed, segmented, and classified. However, these research results have not yet been synthesized to provide coherent guidance on the effect of variant land-cover segmentation processes. In this paper, we present a novel model that augments segmentation using smaller networks to segment individual classes. The combined network is trained on the same data but with the masks, combined and trained using categorical cross entropy. Experimental results show that the proposed method produces the highest mean IoU (Intersection of Union) as compared against several existing state-of-the-art models on the DeepGlobe dataset.

Image registration of multimodal remote sensing images plays a vital role in remote sensing image analysis. However, there are significant nonlinear intensity differences between multimodal remote sensing image pairs, making it difficult for most traditional image registration algorithms to meet the registration requirements. In this paper, we propose a novel edge descriptor utilizing edge information, which has not only affine invariance but is also insensitive to nonlinear intensity differences. Moreover, we utilize the proposed descriptor to design a multimodal image registration algorithm. We use several different multimodal image pairs to evaluate the proposed algorithm. The experimental results show that the proposed algorithm holds a stable performance and can still achieve accurate spatial alignment even with the huge nonlinear intensity differences.

The time dynamics of long-term time series of satellite thermal signal, measured at Mount Etna, has been investigated. The signal has been analyzed by means of a recently proposed multi-temporal and robust technique (RST), which has already shown to be better capable to detect and monitor volcanic hotspots, compared to traditional satellite approaches. The temporal fluctuations of the thermal signal detected by RST over a long series (1995-2005) of Advanced Very High Resolution Radiometer (AVHRR) satellite data, have been characterized by means of the correlation function and the power spectrum analysis, which have shown the presence of correlation structures in the thermal time series recorded in the crater area.

A Bayesian method for estimating directional wave spectra from the Doppler spectra obtained by HF radar is examined using data acquired during the SCAWVEX project. Applicability, validity and accuracy of the Bayesian method are demonstrated compared with the directional spectrum observed by a directional buoy. In addition the estimated spectra are compared with Wyatt (1990) and the Bayesian method is found to be more robust against noise. Necessary conditions of the Doppler spectral components to be used to estimate a reliable directional spectrum for the present method are also discussed.

A high-resolution or complete bathymetric map of shallow water based on sparse point measurements (depth soundings) is often needed. One possible approach to such maps is passive remote sensing of water depth by using multispectral imagery in the popular method proposed by Lyzenga et al. [2006]; however, its application has been limited due to insufficient accuracy. To improve accuracy, we have developed 3 extensions of Lyzenga's method by addressing unrealistic optical and statistical assumptions in the method. The purpose of this paper is to compare the accuracy of Lyzenga's method, the 3 extensions, and the combination of the 3 extensions. The accuracy comparison test was performed for 2 coral reef sites by using cross validation.

The results indicated that for both sites, the extended methods were more accurate than Lyzenga's method when sufficient training data were available. The most accurate extension was the one derived by modeling the spatial autocorrelation in the error term of the regression model used in Lyzenga's method. The combination of the 3 extensions was even more accurate than the extensions.

The implementations of the extended methods are not difficult in terms of software availability and computational cost.

The authors visually inspected the building damage caused by the 2011 Tohoku earthquake tsunami, using the pre and post-event aerial photos. First, we prepared the mosaic of post-tsunami aerial photos acquired by Geospatial Information Authority of Japan (GSI), and conducted the visual inspection of buildings damage to classify the damage. The damage classification results are compiled with building shape files on GIS for mapping the structural vulnerability in the tsunami inundation zone. Finally, we discussed the structural vulnerability in the tsunami affected area based on mapping results of building damage.

UAV (Unmanned Aerial Vehicle) remote sensing technology has been increasingly used to support spatial data acquisition and land management project verification. Currently, in land consolidation field, UAV is mainly applied to acquire high resolution images, which cannot provide support to the whole process of developing projects including site selection, survey, design, implementation, supervision and acceptance. Since the project areas are usually small and dispersed, this paper discusses a new technical development of UAV remote sensing technology to support UAV applications in land consolidation. The new technical development includes spatial data acquisition and rapid processing methods as well as the procedure of large-scale production. At the same time, some key technologies are studied. In order to get the image with high resolution, low altitude aerial route planning technology is studied, taking the regional shape and flight control into account. To improve the image processing efficiency, the CUDA (computer unified device architecture) parallel algorithm is used to make best use of GPU. Besides, 3D point cloud is produced by the dense matching algorithm, which is used to build 3D landscape of the land consolidation area, through which we can make land consolidation plan in a real 3D world, calculate cut-fill earthwork volumes, estimate engineering quantity and cost. By using the dense matching cloud points and the orthophoto maps, some types of natural elements are extracted, such as farmland, water, road, village etc., by image classification and recognition technology, then the current image and the past image are compared to find the land use changes, monitoring the progress and quality of the project. Finally, experiments are performed and the results demonstrate that the proposed method can improve the efficiency of land consolidation projects significantly.

Discrimination between hazardous materials in the environment and ambient constituents is a fundamental problem in environmental sensing. The ubiquity of naturally occurring bacteria, plant pollen, fungi, and other airborne materials makes the task of sensing for biological warfare (BW) agents particularly challenging. The spectroscopic properties of the chemical warfare (CW) agents in the long wavelength infrared (LWIR) region are important physical properties that have been successfully exploited for environmental sensing. However, in the case of BW agents, the LWIR region affords less distinction between hazardous and ambient materials. Recent studies of the THz spectroscopic properties of biological agent simulants, particularly bacterial spores, have yielded interesting and potentially useful spectral signatures of these materials. It is anticipated that with the advent of new THz sources and detectors, a novel environmental sensor could be designed that exploits the peculiar spectral properties of the biological materials. We will present data on the molecular spectroscopy of several CW agents and simulants as well as some THz spectroscopy of the BW agent simulants that we have studied to date, and discuss the prospectus with regard to detection probabilities through the application of sensor system modeling.

A cross-comparison method was used to assess the SPOT-6 optical satellite imagery against Chinese GF-1 imagery using three types of indicators: spectral and color quality, fusion effect and identification potential. More specifically, spectral response function (SRF) curves were used to compare the two imagery, showing that the SRF curve shape of SPOT-6 is more like a rectangle compared to GF-1 in blue, green, red and near-infrared bands. NNDiffuse image fusion algorithm was used to evaluate the capability of information conservation in comparison with wavelet transform (WT) and principal component (PC) algorithms. The results show that NNDiffuse fused image has extremely similar entropy vales than original image (1.849 versus 1.852) and better color quality. In addition, the object-oriented classification toolset (ENVI EX) was used to identify greenlands for comparing the effect of self-fusion image of SPOT-6 and inter-fusion image between SPOT-6 and GF-1 based on the NNDiffuse algorithm. The overall accuracy is 97.27% and 76.88%, respectively, showing that self-fused image of SPOT-6 has better identification capability.