Abstract— This paper proposes a novel feature extraction method for unsupervised multispectralimage segmentation by pruning the two dimensional texture feature named combine neighborhood differences. In contrast with the original CND, which is applied with traditional image, the pruned CND is computed on a single pixel with various bands. The proposed algorithm utilizes the sign of differences between bands of the pixel. The difference values are thresholded to form a binary codeword. A binomial factor is assigned to the codeword to form another unique value. These values are then grouped to construct the multiband CND feature image where is used in the unsupervised segmentation. Experimental results, with respect to segmentation and classification accuracy using two LANDSAT multispectral images test suite have been performed. The result shows that the pruned CND feature outperforms spectral feature, with average classification accuracies of 87.55% whereas that of spectral feature is 55.81%.
The standard data of Worldview-2 owned by LAPAN is Ortho-Ready Standard level 2 (OR2A) data consisting of 4 multispectral bands (blue, green, red, NIR) and one panchromatic band each 2 m and 0,5 m spatial resolution. Both images have different metadata and RPC, making it possible to perform geometric corrections separately. This paper discusses the analysis of the inaccuracies of multispectralimage positions to panchromatic images compared to those that have been systematically geometric corrected. The method used is fast fourier transform phase matching by taking 500 binding points between the two images. The measurement results prove that the multispectralimage of the Worldview-2 data of the OR2A level has a larger shift compared with multispectralimage that has been systematically geometric corrected. The multispectralimage of the OR2A data shifts are 2,14 m on the X-axis and 0,42 m on the Y-axis. While the multispectralimage that has been systematically geometric corrected shifts are 1,72 m on the X-axis and 0,54 m on the Y-axis.
Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be employed to estimate the fractions of reflected light from the different objects within the pixel area. However, spectral unmixing does not provide any spatial information about the sources and therefore additional information is needed to precisely locate the sources. In order to restore the spatial information of hyperspectral images we propose a hyperspectral and multispectralimage fusion method based on spectral unmixing. The algorithm is tested with HyMAP image data consisting of 125 spectral bands and a simulated multispectralimage consisting of 8 bands.
All 4 images are oriented by estimating affine transformation parameters between observed and nominal RPC (rational polynomial coefficients) image positions of 17 ground control points (GCP) and a subsequent calculation of refined RPC. From 10 independent check points RMS errors of 2.2m, 2.0m and 2.7m in X, Y and H are obtained. Subsequently, a DSM of 5m grid spacing is generated fully automatically. A comparison with the Lidar data results in an overall DSM accuracy of approximately 3m. In moderate and flat terrain higher accuracies in the order of 2.5m and better are achieved. In a next step orthoimages from the high resolution nadir image and the multispectralimage are generated using the refined RPC geometry and the DSM. After radiometric corrections a fused high resolution colour orthoimage with 2.1m pixel size is created using an adaptive HSL method. The pansharpen process is performed after the individual geocorrection due to the different viewing angles between the two images. In a detailed analysis of the colour orthoimage artifacts are detected covering an area of 4691ha, corresponding to less than 2% of the imaged area. Most of the artifacts are caused by clouds (4614ha). A minor part (77ha) is affected by colour patch, stripping or blooming effects.
Vegetation height plays a crucial role in various ecological and environmental applications, such as biodiversity assessment and monitoring, landscape characterization, conservation planning and disaster management. Its estimation is traditionally based on in situ measurements or airborne Light Detection And Ranging (LiDAR) sensors. However, such methods are often proven insufficient in covering large area landscapes due to high demands in cost, labor and time. Considering a multispectralimage from a passive satellite sensor as the only available source of information, we propose and evaluate new ways of discriminating vegetated habitat species according to their height, through calculation of texture analysis measures, based on local variance, entropy and local binary patterns. The methodology is applied in a Quickbird image of Le Cesine protected site, Italy. The proposed methods are proven particularly effective in discriminating low and mid phanerophytes from tall phanerophytes, having a height of less and more than 2 meters, respectively. The results indicate a promising alternative in vegetation height estimation when in situ or LiDAR data are not available or affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks.
To fully utilize the spectral information and remove noise in multispectralimage change detection, A fusion-based unsupervised approach, which exploits NSCT (Nonsubsampled Contourlet Transform) and multi-scale saliency maps for detecting changed areas by using multispectral images is presented in this paper. Firstly, aiming at make full use of multispectral information, each band of the multitemporal images is applied to get an initial difference image set (IDIS), which is then decomposed into several low-pass approximation and high-pass directional sub bands by NSCT; In order to remove most of the noise, saliency maps of each sub bands and each scales are obtained by processing only the low-frequency sub-band coefficients of the decomposed image; Finally the binary change map is extracted by using a novel inter-scale and inter-band fusion method. Experimental results validate the superior performance of the proposed approach with respect to several state-of-the-art change detection techniques.
In this paper, Ehlers fusion was used. This method is done with the following procedure. Intensity Hue Saturation (IHS) transformation is done in the multispectralimage. Fast Fourier Transform (FFT) is applied in the component of Intensity (I) of the resulted image as well a low pass filter. Fast Fourier Transform is also applied in the panchromatic image followed by high pass filtering. In these filtered images inverse FFT is applied and the results are added. Finally an inverse HIS transformation is applied to produce a fused RGB image. The procedure can be repeated with successive three band selections until all bands are fused with the panchromatic image (Ehlers, 2010).
Selection of proper fusion technique depends on the specific remote image. Four fusion methods, including Brovey, PCA, Pansharp, and SFIM , are used to fuse the images of multispectral bands and panchromatic band. Three quantitative indicators were calculated and analyzed, that is, gradient, correlation coefficient and deviation. Finally, form the above analysis and comparison, we can conclude that SFIM algorithm can preserve the spectral characteristics of the source multispectralimage as well as the high spatial resolution characteristics of the source panchromatic image and suited for fusion ZY03 and SPOT05 images.
Berbagai upaya dilakukan perusahaan dalam rangka mempertahankan brand image (citra merek) yang mereka miliki diantaranya inovasi teknologi keunggulan yang dimiliki produk tersebut, penetapan harga yang bersaing dan promosi yang tepat sasaran. Semakin baik brand image (citra merek) produk yang dijual maka akan berdampak pada keputusan pembelian oleh konsumen. Keputusan pembelian oleh konsumen adalah keputusan yang melibatkan persepsi terhadap kualitas, nilai dan harga. Konsumen tidak hanya mengunakan harga sebagai tolak ukur kualitas dari produk tetapi biaya yang dikorbankan untuk ditukar dengan produk juga menjadi tolak ukur akan manfaat produk tersebut.
Bukan hanya citra diri yang akan mempengaruhi produk apa yang akan dipilih, tetapi produk yang dikonsumsi juga mempunyai pengaruh terhadap citra diri. Ketika membeli produk yang mempunyai nilai simbolik, maka saat menggunakannya, produk tersebut akan membantu menempatkan citra diri (Nugroho, 2003:145). Arti simbolik yang melekat pada suatu merek, sering dikomunikasikan melalui pemakaian dan penggunaan dari suatu merek (Jamal dan Goode, 2001:482). Menurut Schiffman & Kanuk (2008:127), self-image terdiri dari empat dimensi, yaitu: Actual self-image, Ideal self-image, Social self-image dan Ideal social self-image Dalam konteks retail, actual self- image congruity merupakan kesesuaian antara citra diri actual pembelanja dengan citra toko, ideal self-image congruity adalah adanya kesesuaian antara citra diri ideal pembelanja dan citra toko, social self-image congruity adalah adanya kesesuaian antara citra diri sosial pembelanja dan citra toko, dan ideal social self- image congruity adalah adanya kesesuaian antara citra diri ideal-sosial pembelanja dan citra toko. Citra toko atau kepribadian toko yang didasarkan pada tipe pembelanja/ pelanggan yang datang di suatu toko atau departement store (Sirgy et al., 2009). Konsumen lebih suka mempersepsikan retail secara berbeda berdasarkan tipe pelanggan yang berbelanja di retail tersebut. Konsumen seringkali menyesuaikan antara citra pelanggan mall (retail) tersebut dengan citra dirinya. Proses penyesuaian ini disebut juga self-image
In order to get more information, image fusion techniques are often used to integrate the complementary information among different remote sensing images. By far, a great number of fusion methods for remote sensing images have been developed (Luo et al., 2002; Pohl and van Genderen, 1998). Classical remote sensing image fusion techniques include panchromatic(PAN) / multi-spectral(MS) fusion (Joshi and Jalobeanu, 2010; Li and Leung, 2009), MS / hyper-spectral(HS) fusion (Eismann and Hardie, 2005) and multi-temporal (MT) fusion (Shen et al., 2009) etc. However, most fusion methods were developed to fuse images from two sensors, and little work attempted to solve the fuse problem of more sensors. In this paper, we propose an integrated fusion method for multiple temporal-spatial-spectral scales of remote sensing images. This method is based on the maximum a posteriori (MAP) framework, which has the performance to fuse images from arbitrary number of optical sensors.
for natural landscape images is in the range of 1.00 to 1.51.Among the 8 natural landscape images, as we can see, the smoothest one is No.8a image. As shown in the Table 1, the color FD value of No. 8 natural landscape images is the lowest, 1.00, which is in agreement with the visually impression. From Table 1 we can find that the No. 7natural landscape images is of largest FD value, 1.51, which means the image should be the coarsest one of all 8 natural landscape images. At the same time, from figure 3 we can find that the No.4a image indeed is coarser than other images.
Selanjutnya anda bisa membuka Folder image di sana anda akan menemukan image yang sudah jadi, namun anda perlu mengganti image2 tersebut, untuk itu silahkan buka folder blank image. Nah di sana anda akan mendapatkan file image yang serupa dan nama yang serupa antara folder “image” dan folder “blank image” perbedaan di sini file2 image tersebut masih dalam keadaan kosong, untuk itu silahkan anda edit menggunakan Adobe Photoshop sebenarnya mengunakan paint juga bisa namun saya merekomendasikan editingnya menggunakan adobe photoshop
facial image recognition is one of the biometric technologies is widely studied and developed by the experts . It is because in general, the face image can provide the specific information related to personal identification . However the face image has high variation as the input. In general, these variations are caused by two factors. The first factor is variation on own face and second factor is the variation caused the object transformation of face into face image. The variations of the face image must be able to be resolved by face recognition system .