Xiaodong Hu

Multiscale Water Body Extraction in Urban Environments From Satellite Images

Water is a fundamental element in urban environments, and water body extraction is important for landscape and urban planning. Remote sensing has increasingly been used for water body extraction; however, in urban environments, this kind of approaches is challenging because of the significant within-class spectral variance in water areas and the presence of complex ground features. The objective of this study is to develop an automatic method that could improve water body extraction in urban environments from moderate spatial resolution satellite images. Central to our method is the combined use of multiscale extractions and spectral mixture analysis techniques in adaptive local regions. Specifically, we first calculate the NDWI image from experimental images for selecting water sample pixels. Second, on the basis of the selected water pixels, we apply an improved spectral mixture analysis technique on the experimental image to get water abundance of every pixel, and segment the abundance image to extract water bodies at the global scale. Third, in a similar manner, we iteratively conduct the water body extraction in multiscale local regions to refine the water bodies. Finally, the final result of water bodies is obtained when a stopping criterion is satisfied. We have implemented this method to produce water maps from an ALOS/AVNIR-2 image and a Terra/ASTER image covering urban areas. The experimental results illustrate that the proposed method has substantially outperformed two related methods that use the NDWI-based thresholding and the SVM classification for the entire image.

Segmentation of Multi-spectral Satellite Images Based on Watershed Algorithm

In this paper, a two-step segmentation algorithm is proposed based on watershed transform to segment multi-spectral satellite images. The first step is to use watershed segmentation to gain the initial over-segmented regions and the next one is region merging using a strategy of minimizing the overall heterogeneity increased within segments at each merging step. Textural, color and shape information of segments is used in the merging process. The study was conducted to explore an efficient approach to segment remote sensing images especially for high resolution multi-spectral satellite imagery. Experimental results show that the proposed method can produce quite good segmentation results and is very promising in segmentation of remotely sensing imagery in the future.

Adaptive Scale Selection for Multiscale Segmentation of Satellite Images

With dramatically increasing of the spatial resolution of satellite imaging sensors, object-based image analysis (OBIA) has been gaining prominence in remote sensing applications. Multiscale image segmentation is a prerequisite step that splits an image into hierarchical homogeneous segmented objects for OBIA. However, scale selection remains a challenge in multiscale segmentation. In this study, we presented an adaptive approach for defining and estimating the optimal scale in the multiscale segmentation process. Central to our method is the combined use of image features from segmented objects and prior knowledge from historical thematic maps in a top-down segmentation procedure. Specifically, the whole image was first split into segmented objects, with the largest scale in a presupposition segmentation scale sequence. Second, based on segmented object features and prior knowledge in the local region of thematic maps, we calculated complexity values for each segmented object. Third, if the complexity values of an object were large enough, this object would be further split into multiple segmented objects with a smaller scale in the scale sequence. Then, in the similar manner, complex segmented objects were split into the simplest objects iteratively. Finally, the final segmentation result was obtained and evaluated. We have applied this method on a GF-1 multispectral satellite image and a ZY-3 multispectral satellite image to produce multiscale segmentation maps and further classification maps, compared with the state-of-the-art and the traditional mean shift algorithm. The experimental results illustrate that the proposed method is practically helpful and efficient to produce the appropriate segmented image objects with optimal scales.

Prior Knowledge-Based Automatic Object-Oriented Hierarchical Classification for Updating Detailed Land Cover Maps

Automatic information extraction from optical remote sensing images is still a challenge for large-scale remote sensing applications. For instance, artificial sample collection cannot achieve an automatic remote sensing imagery classification. Based on this, this paper resorts to the technologies of change detection and transfer learning, and further proposes a prior knowledge-based automatic hierarchical classification approach for detailed land cover updating. To establish this method, an automatic sample collection scheme for object-oriented classification is presented. Unchanged landmarks are first located. Prior knowledge of these categories from previously interpreted thematic maps is then transferred to the new target task. The knowledge is utilized to rebuild the relationship between landmark classes and their spatial-spectral features for land cover updating. A series of high-resolution remote sensing images are experimented for validating the effectiveness of the proposed approach in rapidly updating detailed land cover. The results show that, with the assistance of preliminary thematic maps, the approach can automatically obtain reliable object samples for object-oriented classification. Detailed land cover information can be excellently updated with a competitive accuracy, which demonstrates the practicability and effectiveness of our method. It creates a novel way for employing the technologies of knowledge discovery into the field of information extraction from optical remote sensing images.

Adaptive extraction of water in urban areas based on local iteration using high-resolution multi-spectral image

Urban water extraction from high-resolution remote sensing image is one of important aspects for regional-urban environment research. However, the past researches mainly centered upon moderate and low resolution image, water body in open country and global scale for water extraction, which cant achieve better performance. To solve those problems, an adaptive method to extract urban water was proposed based on local iteration using high-resolution remote sensing image. In each iteration, segmented buffers were constructed with adaptive length and radius to exploit information in local scale, and also spatial consistency was token into account for an improvement of local classification. Experiments demonstrated that the proposed method was applicable in urban water extraction from high-resolution image.

A New Approach to Improve the Cluster-based Parallel Processing Efficiency of High-Resolution Remotely Sensed Image

Object-oriented remotely sensed images processing method has been accepted by more and more experts of remote sensing. To advance the efficiency of data processing, parallel image computing is a good choice since large volumes of data need be analyzed efficiently and rapidly. This paper presents the information extraction method based on per-parcel extraction of high-resolution remotely sensed image; to extract efficiently different information from remotely sensed image, this paper gives the research idea of image rough-classification based on large-scale and subtle-segmentation based on small-scale; to improve the efficiency of image processing, we adapt parallel computing method to solve this problem by presenting an new data-partition method. At last this paper gives the implementation of the research idea based on Message Passing Interface (MPI) and analyzes our experimental system efficiency, and the results show that the new methods can improve the efficiency of high-resolution remotely sensed image data processing efficiently and have a good application.

A Thin-Cloud Mask Method for Remote Sensing Images Based on Sparse Dark Pixel Region Detection

Thin clouds in remote sensing images increase the radiometric distortion of land surfaces. The identification of pixels contaminated by thin clouds, known as the thin-cloud mask, is an important preprocessing procedure to guarantee the proper utilization of data. However, failure to effectively separate thin clouds and high-reflective land-cover features causes thin-cloud masks to remain a challenge. To overcome this problem, we developed a thin-cloud masking method for remote sensing images based on sparse dark pixel region detection. As a result of the effect of scattering, the path radiance is added to the radiance recorded by the sensor in the thin-cloud area, which causes the number of dark pixels in the thin-cloud area to be much less than that in the clear area. In this study, the area of a Thiessen polygon (a nonparametric measure) is used to evaluate the density of local dark pixels, and the region with the sparse dark pixel is selected as the thin-cloud candidate. Then, thin-cloud and clear areas are used as samples to train the background suppression haze thickness index (BSHTI) transform parameters, and convert the original multiband images into single-band images. Finally, an accurate thin-cloud mask is obtained for every buffered thin-cloud candidate, via the segmentation of the BSHTI band. Additionally, the multispectral images obtained by the Wide Field View (WFV), on board the Chinese GaoFen1, and the Operational Land Imager (OLI), on board the Landsat 8, are employed to evaluate the performance of the method. The results reveal that the proposed approach can obtain a thin-cloud mask with a high true-value ratio and detection ratio. Thin-cloud masks can satisfy various application demands.

Geo-Parcel Based Crop Identification by Integrating High Spatial-Temporal Resolution Imagery from Multi-Source Satellite Data

Geo-parcel based crop identification plays an important role in precision agriculture. It meets the needs of refined farmland management. This study presents an improved identification procedure for geo-parcel based crop identification by combining fine-resolution images and multi-source medium-resolution images. GF-2 images with fine spatial resolution of 0.8 m provided agricultural farming plot boundaries, and GF-1 (16 m) and Landsat 8 OLI data were used to transform the geo-parcel based enhanced vegetation index (EVI) time-series. In this study, we propose a piecewise EVI time-series smoothing method to fit irregular time profiles, especially for crop rotation situations. Global EVI time-series were divided into several temporal segments, from which phenological metrics could be derived. This method was applied to Lixian, where crop rotation was the common practice of growing different types of crops, in the same plot, in sequenced seasons. After collection of phenological features and multi-temporal spectral information, Random Forest (RF) was performed to classify crop types, and the overall accuracy was 93.27%. Moreover, an analysis of feature significance showed that phenological features were of greater importance for distinguishing agricultural land cover compared to temporal spectral information. The identification results indicated that the integration of high spatial-temporal resolution imagery is promising for geo-parcel based crop identification and that the newly proposed smoothing method is effective.

Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

In high-resolution remote sensing image processing, segmentation is a crucial step that extracts information within the object-based image analysis framework. Because of its robustness, mean-shift segmentation algorithms are widely used in the field of image segmentation. However, the traditional implementation of these methods cannot process large volumes of images rapidly under limited computing resources. Currently, parallel computing models are generally employed for segmentation tasks with massive remote sensing images. This paper presents a parallel implementation of the mean-shift segmentation algorithm based on an analysis of the principle and characteristics of this technique. To avoid the inconsistency on the boundaries of adjacent data chunks, we propose a novel buffer-zone-based data-partitioning strategy. Employing the proposed data-partitioning strategy, two intensively computation steps are performed in parallel on different data chunks. The experimental results show that the proposed algorithm effectively improves the computing efficiency of image segmentation in a parallel computing environment. Furthermore, they demonstrate the practicality of massive image segmentation when computer resources are limited.

Geo-parcel-based geographical thematic mapping using C5.0 decision tree: a case study of evaluating sugarcane planting suitability

Geographical thematic mapping based on spatial information can effectively support scientific decision-making in Geosciences. To obtain finer spatial decision information, this paper proposes a geo-parcel-based thematic mapping methodology for evaluating cash crop planting suitability using C5.0 decision tree (DT). In this study, geo-parcels are utilized as basic mapping units. Multi-source data are firstly employed to increase geo-parcel units’ attributes and a decision table then is constructed under a multi-attribute index system. Next, rules are mined using a C5.0 DT algorithm according to local geo-parcels in this decision table. Finally, rules are referred as thematic-distinguishing knowledge for inferential mapping in global geo-parcels. A case study of sugarcane planting suitability evaluation is conduct based on the proposed methodology. The experimental results showed that the cross-validation accuracy of the rules is 81.34% and the sum of the very suitable area and suitable area in the generated evaluation map is close to that of historical selected high-yield and high-sugar-content sugarcane bases, which indicated that the mapping result is in good agreement with the actual selection situation. These also demonstrate the effectiveness of our method and thus may be extended to other domains requiring fine geographical thematic mapping of cash crop planting suitability.