scholarly journals Delineation of Cocoa Agroforests Using Multi-Season Sentinel-1 SAR Images: Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping

2019 ◽  
Author(s):  
Frederick N. Numbisi ◽  
Frieke M. B. Van Coillie ◽  
Robert De Wulf
Keyword(s):  
Optical Sensors ◽  
Forest Cover ◽  
Spectral Characteristics ◽  
Image Texture ◽  
Optical Data ◽  
Sar Image ◽  
Grey Level ◽  
Sar Images ◽  
Quantization Level ◽  
Class Prediction

Delineating the cropping area of cocoa agroforests is a major challenge for quantifying the contribution of the land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multi-spectral optical images is difficult due to a similarity in the spectral characteristics of their canopy; moreover, optical sensors are largely impeded by the frequent cloud cover in the tropics. This study explores multi-season Sentinel-1 C-band SAR image to discriminate cocoa agroforests from transition forests for a heterogeneous landscape in central Cameroon. We use an ensemble classifier, random forest, to average SAR image texture features of GLCM (Grey Level Co-occurrence Matrix) across seasons; next, we compare classification performance with results from RapidEye optical data. Moreover, we assess the performance of GLCM texture feature extraction at four different grey level quantization: 32bits, 8bits, 6bits, and 4bits. The classification overall accuracy (OA) of texture-based maps outperformed that from an optical image; the highest OA of 88.8% was recorded at 6bits grey level. This quantization level, in comparison to the initial 32bits in SAR images, reduced the class prediction error by 2.9%. Although this prediction gain may be large for the landscape area, the resultant thematic map reveals the decrease and fragmentation of forest cover by cocoa agroforests. According to our classification validation, the Shannon entropy (H) or uncertainty provides a reliable validation for class predictions and reveals detail inference for discriminating inherently heterogeneous vegetation categories. The texture-based classification achieved a reliable accuracy considering the heterogeneity of the landscape and vegetation classes.

scholarly journals Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping.

2019 ◽  
Vol 8 (4) ◽  
pp. 179 ◽  
Author(s):  
Frederick N. Numbisi ◽  
Frieke M. B. Van Coillie ◽  
Robert De Wulf
Keyword(s):  
Optical Sensors ◽  
Spectral Characteristics ◽  
Texture Feature ◽  
Image Texture ◽  
Optical Data ◽  
Grey Level ◽  
Sar Images ◽  
Quantization Level ◽  
Class Prediction ◽  
Sar Imagery

Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification’s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories.

scholarly journals Comparative Analysis of the Global Forest/Non-Forest Maps Derived from SAR and Optical Sensors. Case Studies from Brazilian Amazon and Cerrado Biomes

2021 ◽  
Vol 13 (3) ◽  
pp. 367
Author(s):  
Edson E. Sano ◽  
Paola Rizzoli ◽  
Christian N. Koyama ◽  
Manabu Watanabe ◽  
Marcos Adami ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Forest Cover ◽  
Satellite Image ◽  
Optical Data ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Data Sets ◽  
Image Sampling ◽  
Forest Maps ◽  
Sar Data

Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image analyses or sampling strategies. The German Aerospace Center (DLR) and the Japan Aerospace Exploration Agency (JAXA) also made available their global FNF maps based on synthetic aperture radar (SAR) data. This paper attempted to answer the following scientific question: how comparable are the FNF products derived from optical and SAR data? As test sites we selected the Amazon (tropical rainforest) and Cerrado (tropical savanna) biomes, the two largest Brazilian biomes. Forest estimations from 2015 derived from TanDEM-X (X band; HH polarization) and ALOS-2 (L band; HV polarization) SAR data, as well as forest cover information derived from Landsat 8 optical data were compared with each other at the municipality and image sampling levels. The optical-based forest estimations considered in this study were derived from the MapBiomas project, a Brazilian multi-institutional project to map land use and land cover (LULC) classes of an entire country based on historical time series of Landsat data. In addition to the existing forest maps, a set of 1619 Landsat 8 RGB color composites was used to generate new independent comparison data composed of circular areas with 5-km diameter, which were visually interpreted after image segmentation. The Spearman rank correlation estimated the correlation among the data sets and the paired Mann–Whitney–Wilcoxon tested the hypothesis that the data sets are statistically equal. Results showed that forest maps derived from SAR and optical satellites are statistically different regardless of biome or scale of study (municipality or image sampling), except for the Cerrado´s forest estimations derived from TanDEM-X and ALOS-2. Nevertheless, the percentage of pixels classified as forest or non-forest by both SAR sensors were 90% and 80% for the Amazon and Cerrado biome, respectively, indicating an overall good agreement.

scholarly journals Seismic Damage Recognition Based on Watershed Segmentation of SAR Image Texture Features

2016 ◽  
Author(s):  
Qiang Li ◽  
Lixia Gong ◽  
Jingfa Zhang
Keyword(s):  
Change Detection ◽  
Texture Feature ◽  
Texture Features ◽  
Seismic Damage ◽  
Segmentation Algorithm ◽  
Image Texture ◽  
Sar Image ◽  
Watershed Segmentation ◽  
Sar Images ◽  
Feature Parameters

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

scholarly journals Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program

2015 ◽  
Vol XL-7/W3 ◽  
pp. 417-423 ◽  
Author(s):  
L. Hojas-Gascon ◽  
A. Belward ◽  
H. Eva ◽  
G. Ceccherini ◽  
O. Hagolle ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Forest Canopy ◽  
Forest Cover ◽  
Spectral Characteristics ◽  
Canopy Cover ◽  
Tree Cover ◽  
Optical Data ◽  
Validation Data ◽  
The Impact ◽  
Sentinel 2

The forthcoming European Space Agency’s Sentinel-2 mission promises to provide high (10 m) resolution optical data at higher temporal frequencies (5 day revisit with two operational satellites) than previously available. CNES, the French national space agency, launched a program in 2013, ‘SPOT4 take 5’, to simulate such a dataflow using the SPOT HRV sensor, which has similar spectral characteristics to the Sentinel sensor, but lower (20m) spatial resolution. Such data flow enables the analysis of the satellite images using temporal analysis, an approach previously restricted to lower spatial resolution sensors. We acquired 23 such images over Tanzania for the period from February to June 2013. The data were analysed with aim of discriminating between different forest cover percentages for landscape units of 0.5 ha over a site characterised by deciduous intact and degraded forests. The SPOT data were processed by one extracting temporal vegetation indices. We assessed the impact of the high acquisition rate with respect to the current rate of one image every 16 days. Validation data, giving the percentage of forest canopy cover in each land unit were provided by very high resolution satellite data. Results show that using the full temporal series it is possible to discriminate between forest units with differences of more than 40% tree cover or more. Classification errors fell exclusively into the adjacent forest canopy cover class of 20% or less. The analyses show that forestation mapping and degradation monitoring will be substantially improved with the Sentinel-2 program.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

scholarly journals Structural and optical properties of amorphous Si–Ge–Te thin films prepared by combinatorial sputtering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
C. Mihai ◽  
F. Sava ◽  
I. D. Simandan ◽  
A. C. Galca ◽  
I. Burducea ◽  
...  
Keyword(s):  
Optical Properties ◽  
Data Storage ◽  
Optical Sensors ◽  
Material Selection ◽  
Functional Materials ◽  
Stability Study ◽  
Optical Data Storage ◽  
Optical Data ◽  
Structural And Optical Properties ◽  
Topological Constraint

AbstractThe lack of order in amorphous chalcogenides offers them novel properties but also adds increased challenges in the discovery and design of advanced functional materials. The amorphous compositions in the Si–Ge–Te system are of interest for many applications such as optical data storage, optical sensors and Ovonic threshold switches. But an extended exploration of this system is still missing. In this study, magnetron co-sputtering is used for the combinatorial synthesis of thin film libraries, outside the glass formation domain. Compositional, structural and optical properties are investigated and discussed in the framework of topological constraint theory. The materials in the library are classified as stressed-rigid amorphous networks. The bandgap is heavily influenced by the Te content while the near-IR refractive index dependence on Ge concentration shows a minimum, which could be exploited in applications. A transition from a disordered to a more ordered amorphous network at 60 at% Te, is observed. The thermal stability study shows that the formed crystalline phases are dictated by the concentration of Ge and Te. New amorphous compositions in the Si–Ge–Te system were found and their properties explored, thus enabling an informed and rapid material selection and design for applications.

scholarly journals Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

2021 ◽  
Vol 13 (14) ◽  
pp. 2686
Author(s):  
Di Wei ◽  
Yuang Du ◽  
Lan Du ◽  
Lu Li
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Target Detection ◽  
Scene Recognition ◽  
Attention Mechanism ◽  
Sar Image ◽  
Target Level ◽  
Sar Images ◽  
Training Samples ◽  
Pass Through

The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods.

scholarly journals High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

2021 ◽  
Vol 13 (2) ◽  
pp. 328
Author(s):  
Wenkai Liang ◽  
Yan Wu ◽  
Ming Li ◽  
Yice Cao ◽  
Xin Hu
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Feature Fusion ◽  
Representation Learning ◽  
Sar Image ◽  
Gabor Filtering ◽  
Feature Maps ◽  
Sar Images ◽  
Multi Scale ◽  
Deep Feature

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

Based the Morphological Filtering BP Algorithm of SAR Image Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1486-1490
Author(s):  
Ding Ding Jiang ◽  
De Rong Cai ◽  
Qiang Wei
Keyword(s):  
Image Recognition ◽  
Learning Algorithm ◽  
Image Interpretation ◽  
Practical Significance ◽  
Sar Image ◽  
Detection Capability ◽  
Rotation Invariant ◽  
Sar Images ◽  
Diverse Background ◽  
Morphological Filtering

SAR image recognition is an important content of of aviation image interpretation work. In this paper, the characteristics of SAR images a practical significance of morphological filtering neural network model and its adaptive BP learning algorithm. As can be seen through the experimental results, the algorithm can not only adapt to the complex and diverse background environment, and has a displacement of the same continuous moving target detection capability, telescopic invariant and rotation invariant features.

scholarly journals Synthetic-Aperture Radar Image Despeckling based on Improved Non-Local Means and Non-Subsampled Shearlet Transform

2020 ◽  
Vol 49 (3) ◽  
pp. 299-307
Author(s):  
Zengguo Sun ◽  
Rui Shi ◽  
Wei Wei
Keyword(s):  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Shearlet Transform ◽  
Local Means ◽  
New Type ◽  
Non Local ◽  
Homogeneous Regions ◽  
Transform Domains

When Synthetic-Aperture (SAR) image is transformed into wavelet domain and other transform domains, most of the coefficients of the image are small or zero. This shows that SAR image is sparse. However, speckle can be seen in SAR images. The non-local means is a despeckling algorithm, but it cannot overcome the speckle in homogeneous regions and it blurs edge details of the image. In order to solve these problems, an improved non-local means is suggested. At the same time, in order to better suppress the speckle effectively in edge regions, the non-subsampled Shearlet transform (NSST) is applied. By combining NSST with the improved non-local means, a new type of despeckling algorithm is proposed. Results show that the proposed algorithm leads to a satisfying performance for SAR images.

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