A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification

The successful launch of panchromatic WorldView-1 and the planned launch of WorldView-2 will make a major contribution towards the advancement of the commercial remote sensing industry by providing improved capabilities, more frequent revisits and greater imaging flexibility with respect to the precursor QuickBird satellite. Remote sensing data from panchromatic systems have a potential for more detailed and accurate mapping of the urban environment with details of sub-meter ground resolution, but at the same time, they present additional complexities for information mining.In this study, very high-resolution panchromatic images from QuickBird and WorldView-1 have been used to accurately classify the land-use of four different urban environments: Las Vegas (U.S.A.), Rome (Italy), Washington D.C. (U.S.A.) and San Francisco (U.S.A.). The proposed method is based on the analysis of first- and second-order multi-scale textural features extracted from panchromatic data. For this purpose, textural parameters have been systematically investigated by computing the features over five different window sizes, three different directions and two different cell shifts for a total of 191 input features. Neural Network Pruning and saliency measurements made it possible to determine the most important textural features for sub-metric spatial resolution imagery of urban scenes.The results show that with a multi-scale approach it is possible to discriminate different asphalt surfaces, such as roads, highways and parking lots due to the different textural information content. This approach also makes it possible to differentiate building architectures, sizes and heights, such as residential houses, apartment blocks and towers with classification accuracies above 0.90 in terms of Kappa coefficient computed over more than a million independent validation pixels.     

A quantitative investigation of the uncertainty associated with mapping scale in the production of land-cover/land-use data

ABSTRACT

Mapping scale is an essential issue in land use and land cover (LULC) data production, which always involves the minimum mapping unit (MMU) that stipulated in the product specification. Since the application of MMUs will inevitably cause some inappropriate classification problems, a technique is needed to evaluate the impact on the data outputs. In this study, a novel method is proposed to investigate the classification uncertainty brought by MMUs on LULC data. The omission errors are predicted based on an assumption of the skewed frequency distribution of the LULC patch size, and the commission errors are subsequently computed through the conversion possibilities among different land classes, which can be deduced from the generalization rule. A test is conducted on real data to verify the underlying assumption on the patch size distribution, and the accuracy of the prediction of omission errors is evaluated through a simulation experiment. A case study is also presented to demonstrate the efficiency and feasibility of the proposed method. At the end of this article, the advantages and notes of this method are discussed for further study and application.     

Exploring the addition of Landsat 8 thermal band in land-cover mapping

One focus of remote-sensing studies is obtaining highly accurate land-cover maps, which is essential for quantifying and monitoring changes in the environment. However, thermal data, which can provide auxiliary information, is often ignored in land-cover classification. In this study we compare the performance of different remote-sensing feature combinations with and without the Landsat 8 thermal band (band 10). The results show that overall the thermal feature had a positive effect on mapping land cover. A combination of spectral features, indices and the thermal feature maximized the improvement in accuracy. The proposed classifier was applied to map land cover in an area in Egypt. The thermal feature significantly reduced the confusion between cropland and wetland. The improvement in accuracy obtained by adding the thermal feature was analysed in a time series spanning 1 year. We found that the thermal feature improved the classification accuracy when temperature variations occurred among the different land-cover types. The effect of the thermal feature was also influenced by the land cover; in cloudless conditions, warmer weather can enhance the accuracy improvement of the thermal feature.     

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.     

Analysis of L-band SAR backscatter and coherence for delineation of land-use/land-cover

In this study, we investigated the potential improvement of land-use/land-cover (LU/LC) classification using multidate backscatter intensity as well as interferometric coherence images derived from Advanced Land Observing Satellite phased array L-band synthetic aperture radar data. Four interferometric synthetic aperture radar data pairs in horizontal–horizontal polarizations were processed to obtain backscatter intensity and coherence images. From the analysis of these images, it was observed that backscatter values alone are not sufficient to separate certain LU/LC classes, e.g. forest and mining areas, due to similarities in the associated scattering mechanisms producing similar backscatter values. However, the temporal coherence values from these LU/LC features were found to be distinctly different. Supervised classifications using maximum-likelihood distance were performed with various combinations of data (three-date backscatter intensity and two-date backscatter intensity with corresponding coherence data) to generate LU/LC maps of the study area. The comparison of classification accuracies obtained for different combinations of data indicates that the classification accuracy is improved by adding coherence information to the backscatter intensity data compared to using the multidate backscatter intensity data alone. Thus, the analysis of backscatter intensity along with coherence is a better alternative than using backscatter intensity alone to improve the accuracy in LU/LC classification.     

A hybrid approach to mapping land-use modification and land-cover transition from MODIS time-series data: A case study from the Bolivian seasonal tropics

This study uses a combination of satellite imagery and GIS data, a vegetation map, interview data, and on-site field studies to map detailed natural vegetation to land-use conversion pathways (~ 22,000 possible combinations) in the seasonal tropics of Santa Cruz Department in southeastern Bolivia from 1994 to 2008. We mapped a suite of land-use classes based on the seasonal phenology of double- and single season cropping regimes; pasture; and bare soil cropland (fallow). Analyses focus specifically on the Corredor Bioceánico, which bisects some of the most sensitive and poorly understood ecosystems in the world and indirectly creating one of the most important agricultural region-deforestation hotspots in South America at the present time. Training data to predict class membership were based on MODIS NDVI annual mean, maximum, minimum, and amplitude derived from field observations, semi-structured interviews, and aerial videography. Results show that over 8,000 km² of forest was lost during the 14-year study period. In the first years of cultivation, pasture is the dominant land use, but quickly gives way to cropland. The main findings according to forest type is that transitional forest types on deep and poorly drained soils of alluvial plains have lost the most in terms of percentage area cleared. The resulting transition pathways can potentially provide decision-makers with more detailed insight as to the proximate causes or driving forces of land change in addition to the most threatened forests remaining in the Tierras Bajas and those most likely to be cleared in the Brazilian Shield and Pantanal.     

Parallel processing of an object space for image synthesis using ray tracing

This paper presents a novel parallel processing system for image synthesis using ray tracing. An object space is divided into parts (subspaces), each of which is allocated to a processor. The processor detects, simultaneously the intersections of the surfaces of each object and a fixed number of rays over the whole space, and calculates the local intensity on an object in each subspace. The global intensities of pixels on a screen are calculated by the other kind of processors simultaneously. We also present the optimal data structure, based on an adaptive division algorithm, for parallel processing of the object space.     

Exploring the joint measurability using an information-theoretic approach

We explore the legal purity parameters for the joint measurements. Instead of direct unsharpening the measurements, we perform the quantum cloning before the sharp measurements. The necessary fuzziness in the unsharp measurements is equivalently introduced in the imperfect cloning process. Based on the information causality and the consequent noisy nonlocal computation, one can derive the information-theoretic quadratic inequalities that must be satisfied by any physical theory. On the other hand, to guarantee the classicality, the linear Bell-type inequalities deduced by these quadratic ones must be obeyed. As for the joint measurability, the purity parameters must be chosen to obey both types of inequalities. Finally, the quadratic inequalities for purity parameters in the joint measurability region are derived.     

Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping

Land-cover mapping is an important research topic with broad applicability in the remote-sensing domain. Machine learning algorithms such as Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) have been playing an important role in this field for many years, although deep neural networks are experiencing a resurgence of interest. In this article, we demonstrate early efforts to apply deep learning-based classification methods to large-scale land-cover mapping. Based on the Stacked Autoencoder (SAE), one of the deep learning models, we built a classification framework for large-scale remote-sensing image processing. We adjusted and optimized the model parameters based on our test samples. We compared the performance of the SAE-based approach with traditional classification algorithms including RF, SVM, and ANN with multiple performance analytics. Results show that the SAE classifier trained with an entire set of African training samples achieves an overall classification accuracy of 78.99% when assessed by test samples collected independently of training samples, which is higher than the accuracies achieved by the other three classifiers (76.03%, 77.74%, and 77.86% of RF, SVM, and ANN, respectively) based on the same set of test samples. We also demonstrated the advantages of SAE in prediction time and land-cover mapping results in this study.     

Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests

Using high-spatial-resolution multispectral imagery alone is insufficient for achieving highly accurate and reliable thematic mapping of urban areas. Integration of lidar-derived elevation information into image classification can considerably improve classification results. Additionally, traditional pixel-based classifiers have some limitations in regard to certain landscape and data types. In this study, we take advantage of current advances in object-based image analysis and machine learning algorithms to reduce manual image interpretation and automate feature selection in a classification process. A sequence of image segmentation, feature selection, and object classification is developed and tested by the data sets in two study areas (Mannheim, Germany and Niagara Falls, Canada). First, to improve the quality of segmentation, a range image of lidar data is incorporated in an image segmentation process. Among features derived from lidar data and aerial imagery, the random forest, a robust ensemble classifier, is then used to identify the best features using iterative feature elimination. On the condition that the number of samples is at least two or three times the number of features, a segmentation scale factor has no particular effect on the selected features or classification accuracies. The results of the two study areas demonstrate that the presented object-based classification method, compared with the pixel-based classification, improves by 0.02 and 0.05 in kappa statistics, and by 3.9% and 4.5% in overall accuracy, respectively.     

A hybrid data fusion approach to the hierarchical mapping of land-cover types in the urban core

It is difficult to map land covers in the urban core due to the close proximity of high-rise buildings. This difficulty is overcome with a proposed hybrid, the hierarchical method via fusing PAN-sharpened WorldView-2 imagery with light detection and ranging (lidar) data for central Auckland, New Zealand, in two stages. After all features were categorized into ‘ground’ and ‘above-ground’ using lidar data, ground features were classified from the satellite data using the object-oriented method. Above-ground covers were grouped into four types from lidar-derived digital surface model (nDSM) based on rules. Ground and above-ground features were classified at an accuracy of 94.1% (kappa coefficient or κ = 0.913) and 93.7% (κ = 0.873), respectively. After the two results were merged, the nine covers achieved an overall accuracy of 93.7% (κ = 0.902). This accuracy is highly comparable to those reported in the literature, but was achieved at much less computational expense and complexity owing to the hybrid workflow that optimizes the efficiency of the respective classifiers. This hybrid method of classification is robust and applicable to other scenes without modification as the required parameters are derived automatically from the data to be classified. It is also flexible in incorporating user-defined rules targeting hard-to-discriminate covers. Mapping accuracy from the fused complementary data sets was adversely affected by shadows in the satellite image and the differential acquisition time of imagery and lidar data.     

New semi-automated mapping of asbestos cement roofs using rule-based object-based image analysis and Taguchi optimization technique from WorldView-2 images

The use of asbestos cement (AC) roofing materials is a significant concern because of their deleterious effects on human health and the environment. The main objective of this study was to map AC roofs from WorldView-2 (WV-2) images using object-based image analysis (OBIA). A robust Taguchi optimization technique was used to optimize segmentation parameters for WV-2 images in heterogeneous urban areas. In this research, two subsets of WV-2 satellite image sets were utilized to map AC roofs. Rule-based OBIA framework was developed on the first study area. Different supervised OBIA classifiers, such as Bayes, k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF), were tested on the first image of the study areas to evaluate the performance of a rule-based classifier. Results of the supervised classifiers showed confusion between AC roof class and some urban features, with overall accuracies of 72.21%, 77%, 81.75%, and 82.02% for Bayes, k-NN, SVM, and RF, respectively. To assess the transferability of the proposed method, the adopted classification framework was applied to larger subsets of WV-2 of the second study area. The results of the proposed approach showed outstanding performance, with overall accuracies of 93.10% and 90.74% for the first and second classified images, respectively. The McNemar test emphasized the statistical reliability of rule-based result (in the first site) compared with supervised classification results. Therefore, the proposed framework of using rule-based classification and Taguchi optimization technique provide an efficient and expeditious approach to mapping and monitoring the presence of AC roofs and help local authorities in their decision-making strategies and policies.     

Super resolution land cover mapping of hyperspectral images using the deep image prior-based approach

ABSTRACT

Due to the instantaneous field-of-view (IFOV) of the sensor and diversity of land cover types, some pixels, usually named mixed pixels, contain more than one land cover type. Soft classification can predict the portion of each land cover type in mixed pixels in the absence of spatial distribution. The spatial distribution information in mixed pixels can be solved by super resolution mapping (SRM). Typically, SRM involves two steps: soft class value estimation, which is similar to the image super resolution of image restoration, and land cover allocation. A new SRM approach utilizes a deep image prior (DIP) strategy combined with a super resolution convolutional neural network (SRCNN) to estimate fine resolution fraction images for each land cover type; then, a simple and efficient classifier is used to allocate subpixel land cover types under the constraint of the generated fine fraction images. The proposed approach can use prior information of input images to update network parameters and no longer require training data. Experiments on three different cases demonstrate that the subpixel classification accuracy of the proposed DIP-based SRM approach is significantly better than the three conventional SRM approaches and a transfer learning-based neural network SRM approach. In addition, the DIP-SRM approach performs very robustly about small-area objects within multiple land cover types and significantly reduces soft classification uncertainty. The results of this paper provide an extension for utilizing SRCNN to address SRM issues in hyperspectral images.     

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Although hyperspectral imagery (HSI), which has been applied in a wide range of applications, suffers from very large volumes of data, its uncompressed representation is still preferred to avoid compression loss for accurate data analysis. In this paper, we focus on quality-assured lossy compression of HSI, where the accuracy of analysis from decoded data is taken as a key criterion to assess the efficacy of coding. An improved 3D discrete cosine transform-based approach is proposed, where a support vector machine (SVM) is applied to optimally determine the weighting of inter-band correlation within the quantization matrix. In addition to the conventional quantitative metrics signal-to-noise ratio and structural similarity for performance assessment, the classification accuracy on decoded data from the SVM is adopted for quality-assured evaluation, where the set partitioning in hierarchical trees (SPIHT) method with 3D discrete wavelet transform is used for benchmarking. Results on four publically available HSI data sets have indicated that our approach outperforms SPIHT in both subjective (qualitative) and objective (quantitative) assessments for land-cover analysis in remote-sensing applications. Moreover, our approach is more efficient and generates much reduced degradation for subsequent data classification, hence providing a more efficient and quality-assured solution in effective compression of HSI.     

A graphical approach for the evaluation of land-cover classification procedures

Abstract

A method for evaluating the effectiveness of different feature combinations and training strategies is described. Preliminary tests have been made using two groups of feature combinations derived from SPOT High Resolution Visible (HRV) data and two sets of training samples. The method is objective, and needs no ground confirmation or interaction from the image analyst. It is recommended as a surrogate for detailed accuracy assessment when attempting to find an optimum set of training pixels or feature combinations for image classification.     

Extraction of major object features using VQ clustering for content-based image retrieval

An image representation method using vector quantization (VQ) on color and texture is proposed in this paper. The proposed method is also used to retrieve similar images from database systems. The basic idea is a transformation from the raw pixel data to a small set of image regions, which are coherent in color and texture space. A scheme is provided for object-based image retrieval. Features for image retrieval are the three color features (hue, saturation, and value) from the HSV color model and five textural features (ASM, contrast, correlation, variance, and entropy) from the gray-level co-occurrence matrices. Once the features are extracted from an image, eight-dimensional feature vectors represent each pixel in the image. The VQ algorithm is used to rapidly cluster those feature vectors into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to the object within the image. This method can retrieve similar images even in cases where objects are translated, scaled, and rotated.     

Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data

Recent satellite missions have provided new perspectives by offering high spatial resolution, a variety of spectral properties, and fast revisit rates to the same regions. In this study, we examined the utility of both broadband red-edge spectral information and texture features for classifying paddy rice crops in South Korea into three different growth stages. The rice grown in South Korea can be grouped into early-maturing, medium-maturing, and medium-late-maturing cultivars, and each cultivar is known to have a minimum and maximum productivity. Therefore, the accurate classification of paddy rice crops into a certain time line enables pre-estimation of the expected rice yields. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the paddy rice crops, particularly when single-season image data were used. In contrast, texture information resulted in only minor improvement or even a slight decline in accuracy, although it is known to be advantageous for object-based classification. This was due to the homogeneous nature of paddy rice fields, as different rice cultivars are similar in terms of their morphology. Based on these results, we conclude that the additional spectral information such as the red-edge band is more useful than the texture features to detect different crop conditions in relatively homogeneous rice paddy environments. We therefore confirm the potential of broadband red-edge information to improve the classification of paddy rice crops. However, there is still a need to examine the relationship between textural properties and paddy rice crop parameters in greater depth.     

A cartographic and database approach for land cover/use mapping and generalization from remotely sensed data

Classification of remotely sensed data involves a set of generalization processes, i.e. reality is simplified to a set of a few classes that are relevant to the application under consideration. This article introduces an approach to image classification that uses a class hierarchy structure for mapping unit definition at different generalization levels. This structure is implemented as an operational relational database and allows querying of more detailed land cover/use information from a higher abstraction level, which is that viewed by the map user. Elementary mapping units are defined on the basis of an unsupervised classification process in order to determine the land cover/use classes registered in the remotely sensed data. Mapping unit composition at different generalization levels is defined on the basis of membership values of sampled pixels to land cover/use classes. Unlike fuzzy classifications, membership values are presented to the user at mapping unit level.     

A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images

A hybrid method that incorporates the advantages of supervised and unsupervised approaches as well as hard and soft classifications was proposed for mapping the land use/cover of the Atlanta metropolitan area using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. The unsupervised ISODATA clustering method was initially used to segment the image into a large number of clusters of pixels. With reference to ground data based on 1?:?40?000 colour infrared aerial photographs in the form of Digital Orthophoto Quarter Quadrangle (DOQQ), homogeneous clusters were labelled. Clusters that could not be labelled because of mixed pixels were clipped out and subjected to a supervised fuzzy classification. A final land use/cover map was obtained by a union overlay of the two partial land use/cover maps. This map was evaluated by comparing with maps produced using unsupervised ISODATA clustering, supervised fuzzy and supervised maximum likelihood classification methods. It was found that the hybrid approach was slightly better than the unsupervised ISODATA clustering in land use/cover classification accuracy, most probably because of the supervised fuzzy classification, which effectively dealt with the mixed pixel problem in the low-density urban use category of land use/cover. It was suggested that this hybrid approach can be economically implemented in a standard image processing software package to produce land use/cover maps with higher accuracy from satellite images of moderate spatial resolution in a complex urban environment, where both discrete and continuous land cover elements occur side by side.     

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the west African Sahel

Abstract

Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.