Object-based larch tree-crown delineation using high-resolution satellite imagery

Olgan larch is a traditional construction material used for the renovation of historical timber-frame buildings in China. However, acquiring the necessary large-sized larch trees from old-growth forests has become a challenge in China because of the rare and inaccessible distribution of these trees. In recent years, remote sensing imagery has provided a more effective alternative for delineating tree crowns automatically with high accuracy. In this study, an object-based method for delineating old-growth larch tree crowns using Geoeye-1 imagery is developed. Tree crown delineation results are tested and evaluated by field data. In addition, the correlation between delineated tree-crown and basal areas are quantitatively validated to ensure that the developed method can be applied for estimating the distribution of old-growth larch trees. Results demonstrate that the developed object-based larch tree-crown delineation method is reliable, thus providing a new technique for detecting old-growth larch tree resources in Northeastern China.     

Line-based rational function model for high-resolution satellite imagery

The object-to-image transformation of high-resolution satellite images often involves a rational functional model (RFM). Traditionally, RFM uses point features to obtain the transformation coefficients. Since control lines offer greater flexibility than control points, this study proposes a new RFM approach based on linear features. The proposed methods include direct RFM and bias-compensated RFM using control lines. The former obtains the rational polynomial coefficients (RPCs) directly from control lines, whereas the latter uses sensor-orientated RPCs and control lines to determine compensated coefficients. The line-based RFMs include vector and parametric line representations. The experiments in this study analysed the effects of line number, orientation, and length using simulation and real data. The real data combined three-dimensional building models and high-resolution satellite images, such as IKONOS and QuickBird images. Experimental results show that the proposed algorithms can achieve pixel-level accuracy.     

Destriping high-resolution satellite imagery by improved moment matching

High-resolution satellite imagery is frequently contaminated with vertical and non-periodic stripes, because of the differential sensitivities of the detector elements to incoming radiation, and the imaging mechanism of the pushbroom-type instruments. The high spatial resolution also implies more structural details and limitations in the spectral range and the number of bands. In this article, focusing on these factors, an improved moment-matching method is proposed for the automatic destriping of high-resolution satellite imagery. Two novel approaches – Gaussian mixture model based grey slicing and an adaptive local window – are specifically designed to facilitate the homogeneity of the local image. In this way, the limitation of moment matching is eliminated. The stripes are suppressed and serious artefacts are avoided, while the radiation characteristics and structural details are preserved. The performance of the proposed approach was evaluated and compared both visually and quantitatively, employing Ziyuan-1-02C of China (ZY-1-02C) and Shijian-9A of China (SJ-9A) panchromatic images. The experimental results confirm the superiority of the proposed approach in suppressing stripes while preventing unwanted artefacts and structural distortion.     

A hybrid chaotic blowfish encryption for high-resolution satellite imagery

Multimedia Tools and Applications - In this article, we have designed a new information confidentiality mechanism based on the combination of Blowfish encryption algorithm along with Henon and Chen...     

Deriving fine-scale socioeconomic information of urban areas using very high-resolution satellite imagery

This article presents a new approach to derive fine-scale socioeconomic information of urban areas using very high resolution satellite data. The rationale behind the method is to use high resolution satellite data, capable of resolving urban morphology details, to derive a classification of the image. Thus, it is assumed that there is a relationship between the socioeconomic profile and the urban morphology of an area in terms of availability of green areas, sport facilities, private swimming pools or pavement conditions. The method is tested using a case study of Lima, Peru. Using a sample of ground data, a neural network classifier was applied to a pre-classified image in which entropy had been used to mask extensive, non-built up areas that would otherwise have inserted spurious information into the classifier. The result shows a high correlation (0.70 R ²) when compared with validation data. The good performances also show that a physiographic satellite view of the city reflects the socioeconomic layout of their inhabitants, thus making remote sensing a complementary tool for social research and urban planning. While the parameterization of the problem may differ from one area to another, it is shown that an a priori choice of a few parameters may help to automatically characterize large areas in social terms, thus allowing social inequality and its evolution to be mapped in those areas with limited availability of data. In order to make the method widely applicable, the possibilities and limitations of applying the procedure to other large cities are discussed.     

Detecting changes in high-resolution satellite coastal imagery using an image object detection approach

This article presents a spatial contrast-enhanced image object-based change detection approach (SICA) to identify changed areas using shape differences between bi-temporal high-resolution satellite images. Each image was segmented and intrinsic image objects were extracted from their hierarchic candidates by the proposed image object detection approach (IODA). Then, the dominant image object (DIO) presentation was labelled from the results of optimal segmentation. Comparing the form and the distribution of bi-temporal DIOs by using the raster overlay function, ground objects were recognized as being spatially changed where the corresponding image objects were detected as merged or split into geometric shapes. The result of typical spectrum-based change detection between two images was enhanced by using changed spatial information of image objects. The result showed that the change detection accuracies of the pixels with both attribute and shape changes were improved from 84% to 94% for the strong attribute pixel, and from 36% to 81% for the weak attribute pixel in study area. The proposed approach worked well on high-resolution satellite coastal images.     

Mapping freshwater marsh species distributions using WorldView-2 high-resolution multispectral satellite imagery

Freshwater wetlands are highly diverse, spatially heterogeneous, and seasonally dynamic systems that present unique challenges to remote sensing. Maximum likelihood and support vector machine-supervised classification were compared to map wetland plant species distributions in a deltaic environment using high-resolution WorldView-2 satellite imagery. The benefits of the sensor’s new coastal blue, yellow, and red-edge bands were tested for mapping coastal vegetation and the eight-band results were compared to classifications performed using band combinations and spatial resolutions characteristic of other available high-resolution satellite sensors. Unlike previous studies, this study found that support vector machine classification did not provide significantly different results from maximum likelihood classification. The maximum likelihood classifier provided the highest overall classification accuracy, at 75%, with user’s and producer’s accuracies for individual species ranging from 0% to 100%. Overall, maximum likelihood classification of WorldView-2 imagery provided satisfactory results for species distribution mapping within this freshwater delta system and compared favourably to results of previous studies using hyperspectral imagery, but at much lower acquisition cost and greater ease of processing. The red-edge and coastal blue bands appear to contribute the most to improved vegetation mapping capability over high-resolution satellite sensors that employ only four spectral bands.     

Multi-scale hierarchical sampling change detection using Random Forest for high-resolution satellite imagery

ABSTRACT

High-resolution imagery provides rich information useful for land-use and land-cover change detection; however, methods to exploit these data lag behind data collection technologies. In this article, we propose a novel object-oriented multi-scale hierarchical sampling (MSHS) change detection method for high-resolution satellite imagery. In our method, MSHS is carried out to automatically obtain multi-scale training samples and different sample combinations. The training sample spectra, texture, and shape features are fused to build feature space after MSHS. Sample combinations and corresponding feature spaces are input into Random Forest (RF) to train multiple change classifiers. An optimal RF change detection classifier is selected when the out-of-bag error parameter in RF is at the minimum. In order to validate the proposed method, we applied it to high-resolution satellite image data and compared the detection results from our method and the single-scale sampling change detection method. These experimental results show that false alarm rates and missed detection of changed objects using our method were lower than the single-scale sampling change detection method. To demonstrate the scalability of the algorithm, different change detection methods were applied to three study sites. Experimental results show that our method delivered high overall accuracy and F₁-scores. Compared to traditional methods, our method makes full use of the multi-scale characteristics of ground objects. Our approach does not extend multi-scale feature vectors directly, but instead automatically increases the amount of the training samples at multiple scales, without increasing the volume of manual processing, thus improving the ability of the algorithm to generalize features from the RF model, making it more robust.     

Global DEMs to tackle RPC biases and the overfitting phenomenon in high-resolution satellite imagery

ABSTRACT

The overfitting phenomenon and rational polynomial coefficients (RPCs) biases are two crucial issues that degrade the accuracy of geospatial products derived from high-resolution satellite images. The overfitting phenomenon is caused by both a large number of RPCs and strong correlations among them. The RPC biases arise from uncertainties in the global positioning system receivers and inertial measurement units. In this article, an innovative framework based on the genetic algorithm (GA) and the least squares (LS) algorithm, called GALS, is proposed to overcome these problems simultaneously. In this method, the GA is applied to select the optimum RPCs, while the LS algorithm is used to estimate the values of the optimally selected RPCs. The GALS method requires various sets of well-distributed ground control points (GCPs). To tackle the problem of GCP collection, we generated a large number of digital elevation model (DEM)-derived GCPs (DEMGCPs), using a global DEM (GDEM) and vendor-provided RPCs, refined by only one GCP. To evaluate the performance of this framework, four IRS-P5 data sets were used. The GALS is compared to two competing methods, L1-norm-regularized LS and ridge estimation by considering two scenarios using 50 GCPs and the DEMGCPs. The results demonstrate the superiority of GALS in both scenarios. Furthermore, GALS using DEMGCPs led to far more accurate and stable results when compared to GALS using GCPs. Compared to the vendor-provided RPCs, the results of the GALS using DEMGCPs also indicate a major improvement, single-pixel or subpixel accuracy with around 15 RPCs, and only 1 GCP, in both accuracy and reliability of georeferencing for all IRS-P5 data sets.     

Generation of high-resolution East Antarctic landfast sea-ice maps from cloud-free MODIS satellite composite imagery

A method to generate high spatio-temporal resolution maps of landfast sea ice from cloud-free MODIS composite imagery is presented. Visible (summertime) and thermal infrared (wintertime) cloud-free 20-day MODIS composite images are used as the basis for these maps, augmented by AMSR-E ASI sea-ice concentration composite images (when MODIS composite image quality is insufficient). The success of this technique is dependent upon efficient cloud removal during the compositing process. Example wintertime maximum (~ 374,000 km²) and summertime minimum (~ 112,000 km²) fast-ice maps for the entire East Antarctic coast are presented. The summertime minimum map provides the first high-resolution indication of multi-year fast-ice extent, which may be used to help assess changes in Antarctic sea-ice volume. The 2σ errors in fast-ice extent are estimated to be ± 2.98% when ≥ 90% of the fast-ice pixels in a 20-day period are classified using the MODIS composite, or ± 8.76 otherwise (when augmenting AMSR-E or the previous/next MODIS composite image is used to classify > 10% of the fast ice). Imperfect composite image quality, caused by persistent cloud, inaccurate cloud masking or a highly dynamic fast-ice edge, was the biggest impediment to automating the fast-ice detection procedure.     

Normalization of satellite imagery

Abstract

Sets of Thematic Mapper (TM) imagery taken over the Washington DC metropolitan area during the months of November, March and May were converted into a form of ground reflectance imagery. This conversion was accomplished by adjusting the incident sunlight and view angles and by applying a pixel-by-pixel correction for atmospheric effects. Seasonal colour changes of the area can be better observed when such normalization is applied to space imagery taken in time series. In normalized imagery, the grey scale depicts variations in surface reflectance and tonal signature of multi-band colour imagery can be directly interpreted for quantitative information of the target.     

Mapping wild pear trees (Pyrus bourgaeana) in Mediterranean forest using high-resolution QuickBird satellite imagery

Recent advances in spatial and spectral resolution of satellite imagery as well as in processing techniques are opening new possibilities of fine-scale vegetation analysis with interesting applications in natural resource management. Here we present the main results of a study carried out in Sierra Morena, Cordoba (southern Spain), aimed at assessing the potential of remote-sensing techniques to discriminate and map individual wild pear trees (Pyrus bourgaeana) in Mediterranean open woodland dominated by Quercus ilex. We used high spatial resolution (2.4 m multispectral/0.6 m panchromatic) QuickBird satellite imagery obtained during the summer of 2008. Given the size and features of wild pear tree crowns, we applied an atmospheric correction method, Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and six different fusion ‘pan-sharpening’ methods (wavelet ‘à trous’ weighted transform, colour normalized (CN), Gram–Schmidt (GS), hue–saturation–intensity (HSI) colour transformation, multidirection–multiresolution (MDMR), and principal component (PC)), to determine which procedure provides the best results. Finally, we assessed the potential of supervised classification techniques (maximum likelihood) to discriminate and map individual wild pear trees scattered over the Mediterranean open woodland.     

Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery

A new object-oriented segmentation approach with special focus on shape analysis was developed for the extraction of large, man-made objects, especially agricultural fields, in high-resolution panchromatic satellite imagery. The approach, a combination of region- and edge-based techniques, includes new methods for the evaluation of straight edges, edge preserving degradation, and edge-guided region growing.     

Embedded GPU implementation of sensor correction for on-board real-time stream computing of high-resolution optical satellite imagery

Advanced driver assistance systems applications increasingly use cameras and image processing algorithms. To embed and achieve real-time execution of these algorithms, semiconductor companies propose heterogeneous systems-on-chip (SoCs). Embedding algorithms on this type of hardware is not trivial: One needs to determine how to partition the computational load on the different processing units. In addition, it is not easy to predict whether a given algorithm can be executed on a given heterogeneous SoC while meeting real-time constraints. We propose a novel global methodology to assist with embedding image processing algorithms on heterogeneous SoC while meeting real-time constraints (using a soft real-time analysis). Our approach proposes several heuristics predicting delays and execution times and is based on a set of multi-level test vectors which extract key features of heterogeneous architectures.     

A framework for the segmentation of high-resolution satellite imagery using modified seeded-region growing and region merging

Image segmentation is becoming increasingly important in areas such as object-oriented image classification in the field of remote-sensing image analysis. We present a new approach for the image segmentation of a high-resolution pan-sharpened satellite image based on modified seeded-region growing and region merging. First, we conduct some pre-processing prior to image segmentation to improve segmentation quality. The initial seeds are automatically selected using the proposed block-based seed-selection method. After automatic selection of significant seeds, initial segmentation is achieved by applying the modified seeded-region growing procedure. Finally, region merging, based on a region-adjacency graph, is carried out in post-processing to obtain the final segmentation result. Experimental results demonstrate that the proposed method shows better performance than other approaches, and has good potential for its application to the segmentation of high-resolution satellite imagery.     

A simple method for detection and counting of oil palm trees using high-resolution multispectral satellite imagery

In the past, oil palm density has been determined by manually counting trees every year in oil palm plantations. The measurement of density provides important data related to palm productivity, fertilizer needed, weed control costs in a circle around each tree, labourers needed, and needs for other activities. Manual counting requires many workers and has potential problems related to accuracy. Remote sensing provides a potential approach for counting oil palm trees. The main objective of this study is to build a robust and user-friendly method that will allow oil palm managers to count oil palm trees using a remote sensing technique. The oil palm trees analysed in this study have different ages and densities. QuickBird imagery was applied with the six pansharpening methods and was compared with panchromatic QuickBird imagery. The black and white imagery from a false colour composite of pansharpening imagery was processed in three ways: (1) oil palm tree detection, (2) delineation of the oil palm area using the red band, and (3) counting oil palm trees and accuracy assessment. For oil palm detection, we used several filters that contained a Sobel edge detector; texture analysis co-occurrence; and dilate, erode, high-pass, and opening filters. The results of this study improved upon the accuracy of several previous research studies that had an accuracy of about 90–95%. The results in this study show (1) modified intensity-hue-saturation (IHS) resolution merge is suitable for 16-year-old oil palm trees and have rather high density with 100% accuracy; (2) colour normalized (Brovey) is suitable for 21-year-old oil palm trees and have low density with 99.5% accuracy; (3) subtractive resolution merge is suitable for 15- and 18-year-old oil palm trees and have a rather high density with 99.8% accuracy; (4) PC spectral sharpening with 99.3% accuracy is suitable for 10-year-old oil palm trees and have low density; and (5) for all study object conditions, colour normalized (Brovey) and wavelet resolution merge are two pansharpening methods that are suitable for oil palm tree extraction and counting with 98.9% and 98.4% accuracy, respectively.     

A new rigorous model for high-resolution satellite imagery orientation: application to EROS A and QuickBird

The correct georeferencing of remote sensing imagery is a fundamental task for orthoimages, digital elevation models (DEMs)/digital surface models (DSMs) generation and 3D feature/object extraction. In this article we focus on the georeferencing of pushbroom sensors imagery, in particular single images collected by EROS A and QuickBird satellites, with a rigorous model that is based on the collinearity equations. The model, implemented in the software SISAR (Software per Immagini Satellitari ad Alta Risoluzione), reconstructs the orbital segment during image acquisition through the Keplerian orbital parameters, the sensor attitude, the internal orientation and additional self-calibration parameters. With respect to the estimation procedure, in order to avoid possible instabilities due to high correlations among some parameters leading to design matrix pseudo-singularity, singular value decomposition (SVD) and QR decomposition are used to select the estimable parameters and finally to solve the extended linearized collinearity equations system in the least square (LS) sense.

To test the effectiveness of the new model, SISAR results are compared with the rigorous model implemented in the well-known commercial software OrthoEngine 10.0 (PCI Geomatics, ON, Canada). In this article six images are concerned (two from EROS A and four from QuickBird), showing that SISAR performances are at the level of the OrthoEngine ones.     

Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones

In New Caledonia (21°S, 165°E), shade-grown coffee plantations were abandoned for economic reasons in the middle of the 20th century. Coffee species (Coffea arabica, C. canephora and C. liberica) were introduced from Africa in the late 19th century, they survived in the wild and spontaneously cross-hybridized. Coffee species were originally planted in native forest in association with leguminous trees (mostly introduced species) to improve their growth. Thus the canopy cover over rustic shade coffee plantations is heterogeneous with a majority of large crowns, attributed to leguminous trees. The aim of this study was to identify suitable areas for coffee inter-specific hybridization in New Caledonia using field based environmental parameters and remotely sensed predictors. Due to the complex structure of tropical vegetation, remote sensing imagery needs to be spatially accurate and to have the appropriate bands for monitoring vegetation cover. Quickbird panchromatic (black and white) imagery at 0.6 to 0.7 m spatial resolutions and multispectral imagery at 2.4 m spatial resolution were pansharpened and used for this study. The two most suitable remotely sensed indicators, canopy heterogeneity and tree crown size, were acquired by the sequential use of tree crown detection (neural network), image processing (such as textural analysis) and classification. All models were supervised and trained on learning data determined by human expertise. The final model has two remotely sensed indicators and three physical parameters based on the Digital Elevation Model: elevation, slope and water flow accumulation. Using these five predictive variables as inputs, two modelling methods, a decision tree and a neural network, were implemented. The decision tree, which showed 96.9% accuracy on the test set, revealed the involvement of ecological parameters in the hybridization of Coffea species. We showed that hybrid zones could be characterized by combinations of modalities, underlining the complexity of the environment concerned. For instance, forest heterogeneity and large crown size, steep slopes (> 53.5%) and elevation between 194 and 429 m asl, are favourable factors for Coffea inter-specific hybridization. The application of the neural network on the whole area gave a predictive map that distinguished the most suitable areas by means of a nonlinear continuous indicator. The map provides a confidence level for each area. The most favourable areas were geographically localized, providing a clue for the detection and conservation of favourable areas for Coffea species neo-diversity.     

Searching satellite imagery with integrated measures

Due to the advances in imaging and storage technologies, the number and size of images continue to grow at a rapid pace. This problem is particularly acute in the case of remotely sensed imagery. The continuous stream of sensory data from satellites poses major challenges in storage and retrieval of the satellite imagery. In the mean time, the ubiquity of Internet has resulted into an ever-growing population of users searching for various forms of information. In this paper, we describe the search engine SIMR—Satellite Image Matching and Retrieval system. SIMR provides an efficient means to match remotely sensed imagery. It computes spectral and spatial attributes of the images using a hierarchical representation. A unique aspect of our approach is the coupling of second-level spatial autocorrelation with quad tree structure. The efficiency of the web-based SIMR has been evaluated using a database of images with known characteristics: cities, towns, airports, lakes, and mountains. Results show that the integrated signature can be an effective basis for accurately searching databases of satellite based imagery.