Object-Based vs. Pixel-Based Classification of Mangrove Forest Mapping in Vien An Dong Commune, Ngoc Hien District, Ca Mau Province Using VNREDSat-1 Images

Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune.

Infrared LSS-Target Detection Via Adaptive TCAIE-LGM Smoothing and Pixel-Based Background Subtraction

Infrared small target detection is a significant and challenging topic for daily security. This paper proposes a novel model to detect LSS-target (low altitude, slow speed, and small target) under the complicated background. Firstly, the fundamental constituents of an infrared image including the complexity and entropy are calculated, which are invoked as adaptive control parameters of smoothness. Secondly, the adaptive L0 gradient minimization smoothing based on texture complexity and information entropy (TCAIE-LGM) is proposed in order to remove noises and suppress low-amplitude details in infrared image abstraction. Finally, difference of Gaussian (DoG) map is incorporated into the pixel-based adaptive segmentation (PBAS) background modeling algorithm, which can differ LSS-target from the sophisticated background. Experimental results demonstrate that the proposed novel model has a high detection rate and produces fewer false alarms, which outperforms most state-of-the-art methods.

Evaluating pixel-based vs.object-based image analysis approaches for lithological discrimination using VNIR data of WorldView-3

The object-based against pixel-based image analysis approaches were assessed for lithological mapping in a geologically complex terrain using Visible Near Infrared(VNIR)bands of WorldView-3(WV-3)satellite imagery.The study area is Hormuz Island,southern Iran,a salt dome composed of dominant sedimentary and igneous rocks.When performing the object-based image analysis(OBLA)approach,the textural and spectral characteristics of lithological features were analyzed by the use of support vector machine(SVM)algorithm.However,in the pixelbased image analysis(PBIA),the spectra of lithological end-members,extracted from imagery,were used through the spectral angle mapper(SAM)method.Several test samples were used in a confusion matrix to assess the accuracy of classification methods quantitatively.Results showed that OBIA was capable of lithological mapping with an overall accuracy of 86.54%which was 19.33%greater than the accuracy of PBIA.OBIA also reduced the salt-and-pepper artifact pixels and produced a more realistic map with sharper lithological borders.This research showed limitations of pixel-based method due to relying merely on the spectral characteristics of rock types when applied to high-spatial-resolution VNIR bands of WorldView-3 imagery.It is concluded that the application of an object-based image analysis approach obtains a more accurate lithological classification when compared to a pixel-based image analysis algorithm.

Double Polarization SAR Image Classification based on Object-Oriented Technology

This paper proposed to use double polarization synthetic aperture radar (SAR) image to classify surface feature, based on DEM. It takes fully use of the polarization information and external information. This pa-per utilizes ENVISAT ASAR APP double-polarization data of Poyang lake area in Jiangxi Province. Com-pared with traditional pixel-based classification, this paper fully uses object features (color, shape, hierarchy) and accessorial DEM information. The classification accuracy improves from the original 73.7% to 91.84%. The result shows that object-oriented classification technology is suitable for double polarization SAR’s high precision classification.

Point Selection for Triangular 2-D Mesh Design Using Adaptive Forward Tracking Algorithm

Two-dimensional mesh-based motion tracking preserves neighboring relations (through connectivity of the mesh) and also allows warping transformations between pairs of frames;thus, it effectively eliminates blocking artifacts that are common in motion compensation by block matching. However, available uniform 2-D mesh model enforces connec-tivity everywhere within a frame, which is clearly not suitable across occlusion boundaries. To overcome this limitation, BTBC (background to be covered) detection and MF (model failure) detection algorithms are being used. In this algorithm, connectivity of the mesh elements (patches) across covered and uncovered region boundaries are broken. This is achieved by allowing no node points within the background to be covered and refining the mesh structure within the model failure region at each frame. We modify the occlusion-adaptive, content-based mesh design and forward tracking algorithm used by Yucel Altunbasak for selection of points for triangular 2-D mesh design. Then, we propose a new triangulation procedure for mesh structure and also a new algorithm to justify connectivity of mesh structure after motion vector estimation of the mesh points. The modified content-based mesh is adaptive which eliminates the necessity of transmission of all node locations at each frame.

Assessment of different topographic correction methods in ALOS AVNIR-2 data over a forest area

Because the removal of topographic effects is one the most important preprocessing steps when extracting information from satellite images in digital Earth applications,the problem of differential terrain illumination on satellite imagery has been investigated for at least 20 years.As there is no superior topographic correction method applicable to all areas and all images,a comparison of topographic normalization methods in different regions and images is necessary.In this study,common topographic correction methods were applied on an ALOS AVNIR-2 image of a rugged forest area,and the results were evaluated through different criteria.The results show that the simple correction methods[Cosine,Sun-Canopy-sensor(SCS),and Minnaert correction]are inefficient in exceptionally rough forests.Among the improved correction methods(SCSC,modified Minnaert,and pixel-based Minnaert),the best result was achieved using a pixel-based Minnaert approach in which a separate correction factor in various slope angles is used.Thus,this method should be considered for topographic correction,especially in forests with severe topography.

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Changes of Land Use and Land Cover(LULC)affect atmospheric,climatic,and biological spheres of the earth.Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity reduction.This paper examined effects of pansharpening and atmospheric correction on LULC classification.Object-Based Support Vector Machine(OB-SVM)and Pixel-Based Maximum Likelihood Classifier(PB-MLC)were applied for LULC classification.Results showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral image.Nevertheless,pansharpening plays much more important roles on the classification accuracy than the atmospheric correction.It can help to increase classification accuracy by 12%on average compared to the ones without pansharpening.PB-MLC and OB-SVM achieved similar classification rate.This study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is 82%and 89%respectively.A combination of atmospheric correction,pansharpening,and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images.

Deep learning for land use and land cover classification from the Ecuadorian Paramo.

The paramo,plays an important role in our ecosystems as They balance the water resources and can retain substantial quantities of carbon.This research was carried out in the province of Tungurahua,specifically the Quero district.The aim is to develop a classification of the land use land cover(LULC)in the paramo using satellite imagery using several classifiers and determine which one obtains the best performance,for which three different approaches were applied:Pixel-Based Image Analysis(PBIA),Geographic Object-Based Image Analysis(GEOBIA),and a Deep Neural Network(DNN).Various parameters were used,such as the Normalized Difference Vegetation Index(NDVI),the Bare Soil Index(BSI),texture,altitude,and slope.Seven classes were used:paramo,pasture,crops,herbaceous vegetation,urban,shrubrainland,and forestry plantations.The data was obtained with the help of onsite technical experts,using geo-referencing and reference maps.Among the models used the highest-ranked was DNN with an overall precision of 87.43%,while for the paramo class specifically,GEOBIA reached a precision of 95%.

ClustNails:Visual Analysis of Subspace Clusters

Subspace clustering addresses an important problem in clustering multi-dimensional data. In sparse multi-dimensional data, many dimensions are irrelevant and obscure the cluster boundaries. Subspace clustering helps by mining the clusters present in only locally relevant subsets of dimensions. However, understanding the result of subspace clustering by analysts is not trivial. In addition to the grouping information, relevant sets of dimensions and overlaps between groups, both in terms of dimensions and records, need to be analyzed. We introduce a visual subspace cluster analysis system called ClustNails. It integrates several novel visualization techniques with various user interaction facilities to support navigating and interpreting the result of subspace clustering. We demonstrate the effectiveness of the proposed system by applying it to the analysis of real world data and comparing it with existing visual subspace cluster analysis systems.