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[ACM Press the 2018 International Conference - Tianjin, China (2018.09.19-2018.09.21)] Proceedings of the 2018 International Conference on Electronics and Electrical Engineering Technology - EEET '18 - An Exploratory Analysis of Speckle Noise Removal Methods for Satellite Images
摘要： image processing procedure, Satellite images captured in a variety of modalities serve as the primary source for many applications. Satellite image processing extracts the image /spectral information represented in the form of pixels, classifies those pixels based on the similarity measures and further analyzes the inherent data, as per the requirements. The foremost objective of satellite processing is to automatically categorize the pixels in an image into the respective land cover class labels or themes. These pixels are classified by its spectral information and it is determined by the relative reflectance in various bands of wavelength. The accuracy and outcomes of any satellite the application domain, directly depends on its quality. Satellite images are invariably degraded by speckle noise. Hence, preprocessing the images for speckle noise suppression and/or cloud removal is deemed an inevitable component in satellite image processing. Researchers have proposed a spectrum of methods for speckle noise/cloud removal. A detailed review on the significant research publications on speckle noise removal are summarized in this article. The consolidation of methodology merits and demerits of the select research articles are presented in this paper. This review article on speckle noise removal is designed as a ready-reference for those researchers working in satellite image processing. irrespective of
关键词： RADAR,SAR,Review,Speckle Noise,Satellite images,Noise filters,Literature Survey
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Ship Detection Without Sea-Land Segmentation for Large-Scale High-Resolution Optical Satellite Images
摘要： Ship detection is an important and challenging topic in remote sensing applications. In current literatures, sea-land segmentation is generally requested before ship detection. This makes the implementation of the methods highly complicated. Therefore, based on Faster R-CNN, this paper proposes a ship detection method for large-scale images, which does not need sea-land segmentation as pre-processing step and can detect ships directly from complicated background including sea and land. We use large-scale images consisting of GF-1 and GF-2 satellite images to test our network. Experimental results prove that the proposed method plays a role in removing the interference of objects on land.
关键词： Ship detection,deep learning,sea–land segmentation,high-resolution satellite images
更新于2025-09-23 15:21:21
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Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal
摘要： The total or partial removal of sugarcane (Saccharum spp. L.) straw for bioenergy production may deplete soil quality and consequently affect negatively crop yield. Plants with lower yield potential may present lower concentration of leaf-tissue nutrients, which in turn changes light reflectance of canopy in different wavelengths. Therefore, vegetation indexes, such as the normalized difference vegetation index (NDVI) associated with concentration of leaf-tissue nutrients could be a useful tool for monitoring potential sugarcane yield changes under straw management. Two sites in S?o Paulo state, Brazil were utilized to evaluate the potential of NDVI for monitoring sugarcane yield changes imposed by different straw removal rates. The treatments were established with 0%, 25%, 50%, and 100% straw removal. The data used for the NDVI calculation was obtained using satellite images (CBERS-4) and hyperspectral sensor (FieldSpec Spectroradiometer, Malvern Panalytical, Almelo, Netherlands). Besides sugarcane yield, the concentration of the leaf-tissue nutrients (N, P, K, Ca, and S) were also determined. The NDVI efficiently predicted sugarcane yield under different rates of straw removal, with the highest performance achieved with NDVI derived from satellite images than hyperspectral sensor. In addition, leaf-tissue N and P concentrations were also important parameters to compose the prediction models of sugarcane yield. A prediction model approach based on data of NDVI and leaf-tissue nutrient concentrations may help the Brazilian sugarcane sector to monitor crop yield changes in areas intensively managed for bioenergy production.
关键词： vegetation index,satellite images,yield monitoring,hyperspectral sensor,crop residue management,remote sensing
更新于2025-09-19 17:15:36
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Landcover classification of satellite images based on an adaptive interval fuzzy c-means algorithm coupled with spatial information
摘要： Landcover classifications have large uncertainty related to the heterogeneity of similar objects and complex spatial correlations in satellite images, making it difficult to obtain ideal classification results using traditional classification methods. Therefore, to address the uncertainty in landcover classifications based on remotely sensed information, we propose a novel fuzzy c-means algorithm, which integrates adaptive interval-valued modelling and spatial information. It dynamically adjusts the interval width according to the fuzzy degree of the target membership without pre-setting any parameters, controls the fuzziness of the target, and mines the inherent distribution of the data. Furthermore, reliability-based spatial correlation modelling is used to describe the spatial relationship of the target and to improve both robustness and accuracy of the algorithm. Experimental data consisting of SPOT5 (10-m spatial resolution) or Thematic Mapper (30-m spatial resolution) satellite data for three case study areas in China are used to test this algorithm. Compared with other state-of-the-art fuzzy classification methods, our algorithm markedly improved the ground-object separability. Moreover, it balanced improvement of pixel separability and suppression of heterogeneity of intra-class objects, producing more compact landcover areas and clearer boundaries between classes.
关键词： satellite images,spatial information,adaptive interval fuzzy c-means algorithm,Landcover classification
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Multi-Discriminator Generative Adversarial Network for High Resolution Gray-Scale Satellite Image Colorization
摘要： Automatic colorization for gray-scale satellite images can help with eliminating lighting differences between multi-spectral captures, and provides strong prior information for ground type classification and object detection. In this paper, we introduced a novel generative adversarial network with multiple discriminators for colorizing gray-scale satellite images with pseudo-natural appearances. Although being powerful, deep generative model in its common form with a single discriminator could be unstable for achieving spatial consistency on local textured regions, especially highly textured ones. To address this issue, the generator in our proposed structure produces a group of colored outputs from feature maps at different scale levels of the network, each being supervised by an independent discriminator to fit the original colored training input in discrete Lab color space. The final colored output is a cascaded ensemble of these preceding by-products via summation, thus the fitting errors are reduced by a geometric series form. Quantitative and qualitative comparisons with the sole-discriminator version have been performed on high-resolution satellite images in experiments, where significant reductions in prediction errors have been observed.
关键词： gray-scale satellite images,generative adversarial network,Pseudo-natural colorization,multiple discriminators
更新于2025-09-10 09:29:36
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[IEEE 2018 Conference on Emerging Devices and Smart Systems (ICEDSS) - Tiruchengode, India (2018.3.2-2018.3.3)] 2018 Conference on Emerging Devices and Smart Systems (ICEDSS) - Urban Sprawl Classification Analysis Using Image Processing Technique in Geoinformation System
摘要： Urban Area has become peak attention around the world. Most of the countries are facing challenges in its growth, its impact and its resource management. Satellite images are used to monitor and control the growth of urban sprawl pattern. In this paper, satellite images of Salem city from tamilnadu, India has been considered as a study area for the analysis. Histogram equalization has been applied to the lansat satellite image of Salem city. Followed with the feature extraction in signature method .The extracted features like Built-up land, Mining, Agriculture crop land, Agriculture follow Land, water land, forest are applied to maximum likelihood classification technique. It provide better result as compared to other existing technique.Urbangrowth has been predicted and its direction for extension of land area has been identified. The detected changes are analyzed from the year 1973to 2016.
关键词： Urban sprawl,Geographical Information system (GIS),Salem city,Landsat satellite images
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Deep Domain Adaptation for Single-Shot Vehicle Detector in Satellite Images
摘要： In this paper, we designed unsupervised domain adaptation (DA) methods to vehicle detection in high-resolution satellite images. We applied two Single Shot MultiBox Detectors, which have advantages in handling image feature differences among various kinds of image data: Correlation Alignment DA (CORAL DA) and adversarial DA. These novel methods can much improve accuracy without annotated data by finding the common feature space of source and target domains and aligning the features. While a mean of average precision (AP) and F1 dropped from 84.1% in the source domain to 66.3% in the target domain, the CORAL DA and adversarial DA improved it to 76.8% and 75.9% respectively. These improvements were over a half of the performance degradation, indicating the usability of our methods.
关键词： CORAL,domain adaptation,vehicle detection,satellite images,single shot multibox detector (SSD),adversarial training
更新于2025-09-09 09:28:46
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SéRIES TEMPORAIS DE íNDICES DE VEGETA??O (NDVI E EVI) DO SENSOR MODIS PARA DETEC??O DE DESMATAMENTOS NO BIOMA CERRADO
摘要： The Cerrado is a biome characterized by a wide variety of vegetation formations, strong seasonality and high anthropogenic pressure. This study analyzed the use of time series (2000-2013) of MODIS EVI and MODIS NDVI to detect deforestation in the Cerrado. The study areas corresponded to the municipalities of Jataí/GO, Luís Eduardo Magalh?es/BA, Mateiros/TO and S?o Miguel do Araguaia/GO. The time series were smoothed by the double logistic filter, available in the TIMESAT program. Kruskal-Wallis statistics was used to determine whether the representative temporal signatures of forestlands from Jataí and S?o Miguel do Araguaia and shrublands from all municipalities were statistically equal. Then the deforestation thresholds were identified for each vegetation formation (EVI and NDVI values below of which are considered deforestation). Results indicated that it is not possible to define a single threshold for each type of vegetation, however, it is possible to detect deforestation in forestlands and shrublands. The performance of NDVI was higher than that from EVI since the decrease in the NDVI values during the events of deforestation was higher.
关键词： Satellite Images,Tropical Savanna,Remote Sensing,Temporal Signatures
更新于2025-09-09 09:28:46
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Satellite-based forest monitoring: spatial and temporal forecast of growing index and short-wave infrared band
摘要： For detecting anomalies or interventions in the field of forest monitoring we propose an approach based on the spatial and temporal forecast of satellite time series data. For each pixel of the satellite image three different types of forecasts are provided, namely spatial, temporal and combined spatio-temporal forecast. Spatial forecast means that a clustering algorithm is used to group the time series data based on the features normalised difference vegetation index (NDVI) and the short-wave infrared band (SWIR). For estimation of the typical temporal trajectory of the NDVI and SWIR during the vegetation period of each spatial cluster, we apply several methods of functional data analysis including functional principal component analysis, and a novel form of random regression forests with online learning (streaming) capability. The temporal forecast is carried out by means of functional time series analysis and an autoregressive integrated moving average model. The combination of the temporal forecasts, which is based on the past of the considered pixel, and spatial forecasts, which is based on highly correlated pixels within one cluster and their past, is performed by functional data analysis, and a variant of random regression forests adapted to online learning capabilities. For evaluation of the methods, the approaches are applied to a study area in Germany for monitoring forest damages caused by wind-storm, and to a study area in Spain for monitoring forest fires.
关键词： Satellite images,Functional time series analysis,Forest monitoring,Online random regression forests,Autoregressive integrated moving average
更新于2025-09-04 15:30:14
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A Study on Retrieval Algorithm of Black Water Aggregation in Taihu Lake Based on HJ-1 Satellite Images
摘要： The phenomenon of black water aggregation (BWA) occurs in inland water when massive algal bodies aggregate, die, and react with the toxic sludge in certain climate conditions to deprive the water of oxygen. This process results in the deterioration of water quality and damage to the ecosystem. Because charge coupled device (CCD) camera data from the Chinese HJ environmental satellite shows high potential in monitoring BWA, we acquired four HJ-CCD images of Taihu Lake captured during 2009 to 2011 to study this phenomenon. The first study site was selected near the Shore of Taihu Lake. We pre-processed the HJ-CCD images and analyzed the digital number (DN) gray values in the research area and in typical BWA areas. The results show that the DN values of visible bands in BWA areas are obviously lower than those in the research areas. Moreover, we developed an empirical retrieving algorithm of BWA based on the DN mean values and variances of research areas. Finally, we tested the accuracy of this empirical algorithm. The retrieving accuracies were89.9%, 58.1%, 73.4%, and 85.5%, respectively, which demonstrates the efficiency of empirical algorithm in retrieving the approximate distributions of BWA.
关键词： Taihu Lake,Retrieval Algorithm,Black Water Aggregation,HJ-1 Satellite Images
更新于2025-09-04 15:30:14