The advent of high resolution spaceborne images leads to the development of efficient detection of complex urban details with high precision. This urban land use study is focused on building extraction and height estimation from spaceborne optical imagery. The advantages of such methods include 3D visualization of urban areas, digital urban mapping, and GIS databases for decision makers. In particular, a hybrid approach is proposed for efficient building extraction from optical multi-angular imagery, where a template matching algorithm is formulated for automatic estimation of relative building height, and the relative height estimates are utilized in conjunction with a support vector machine (SVM)-based classifier for extraction of buildings from non-buildings. This approach is tested on ortho-rectified Level-2a multi-angular images of Rio de Janeiro from WorldView-2 sensor. Its performance is validated using a 3-fold cross validation strategy. The final results are presented as a building map and an approximate 3D model of buildings. The building detection accuracy of the proposed method is improved to 88%, compared to 83% without using multi-angular information.

By employing wavelet and selected features (WSF), median merging (MM), and selected curve-fitting (SCF) techniques, the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) has been improved. The PERSIANN-CCS methodology includes the following four main steps: 1) segmentation of satellite cloud images into cloud patches, 2) feature extraction, 3) classification of cloud patches, and 4) derivation of the temperature–rain-rate (T–R) relationship for every cluster. The enhancements help improve step 2 by employing WSF, and step 4 by employing MM and SCF. For the study area herein, the results show that the enhanced methodology improves the equitable threat score (ETS) of the daily and hourly rainfall estimates mostly in the winter and fall. The ETS percentage improvement is about 20% for the daily (10% for hourly) estimates in the winter, 10% for the daily (8% for hourly) estimates in the fall, and at most 5% for the daily estimates in the summer at some rainfall thresholds. In the winter and fall, the area bias is improved almost at all rainfall thresholds for daily and hourly estimates. However, no significant improvement is obtained in the spring, and the area bias in the summer is also greater than that of the implemented PERSIANN-CCS algorithm.

In this paper, the link-based cluster ensemble (LCE) method is utilized to improve cloud classification and satellite precipitation estimation. High resolution Satellite Precipitation Estimation (SPE) is based on the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. This modified SPE with the incorporation of LCE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering cloud patches using LCE; and 4) dynamic application of brightness temperature (Tb) and rain-rate relationships, derived using satellite observations. In order to cluster the cloud patches, the LCE method combines multiple data partitions from different clustering methods. The results show that using the cluster ensemble increases the performance of rainfall estimates compared to the SPE algorithm using a Self Organizing Map (SOM) neural network. The false alarm ratio (FAR), probabilities of detection (POD), equitable threat score (ETS), and bias are used as quantitative measures to assess the performance of the algorithm. It is shown that both the ETS and bias provide improvement in the summer and winter seasons. Almost 5% ETS improvement is obtained at some threshold values for the winter season using the cluster ensemble.

Remotely sensed data (such as hyperspectral imagery) is typically associated with a large number of features, which makes classification challenging. Feature subset selection is an effective approach to alleviate the curse of dimensionality when the number of features contained in datasets is huge. Considering the merits of genetic algorithms (GA) in solving combinatorial problems, GA is becoming an increasingly popular tool for feature subset selection. Most algorithms presented in the literature using GA for feature subset selection use the training classification accuracy of a specific algorithm as the fitness function to optimize over the space of possible feature subsets. Such algorithms require a large amount of time to search for an optimal feature subset. In this paper, we will present a new approach called Genetic Algorithm based Linear Discriminant Analysis (GA-LDA) to extract features in which feature selection and feature extraction are performed simultaneously to alleviate over-dimensionality and result in a useful and robust feature space. Experimental results with classification tasks involving both hyperspectral imagery and SAR data indicate that GA-LDA can result in very low-dimensional feature subspaces yielding high classification accuracies.

Earthen levees have a significant role in protecting large areas of inhabited and cultivated land in the United States from flooding. Failure of the levees can result in loss of life and property. Slough slides are among the problems which can lead to complete levee failure during a high water event. In this paper, we develop a method to detect such slides using X-band synthetic aperture radar (SAR) data. Our proposed methodology includes: radiometric normalization of the TerraSAR image using high-resolution digital elevation map (DEM) data; extraction of features including backscatter and texture features from the levee; a feature selection method based on minimum redundancy maximum relevance (mRMR); and training a support vector machine (SVM) classifier and testing on the area of interest. To validate the proposed methodology, ground-truth data are collected from slides and healthy areas of the levee. The study area is part of the levee system along the lower Mississippi River in the United States. The output classes are healthy and slide areas of the levee. The results show the average classification accuracies of approximately 0.92 and Cohen's kappa measures of 0.85 for both healthy and slide pixels using ten optimal features selected by mRMR with a sigmoid SVM. A comparison of the SVM performance to the maximum likelihood (ML) and back propagation neural network (BPNN) shows that the average accuracy of the SVM is superior to that of the BPNN and ML classifiers.