In this paper, we review and compare the state-of-the-art target detection algorithms. We introduce a new target
detection workflow incorporating a Minimum Noise Fraction (MNF) transform before target detection. Applying a MNF
transform was found to improve the detection results in general, especially with the Orthogonal Subspace Projection
detector. In this paper, we propose a new algorithm - Mixture Tuned Target-Constrained Interference-Minimized Filter
(MTTCIMF). MTTCIMF uses the MNF transformed image as the input and combines the mixture tuned technique with
the TCIMF target detector. By adding an additional infeasibility band, mixture tuned techniques improve the detection
results with a reduced number of false alarms. A HyMap data set with ground truth is used in the comparative study.
Quantitative and visual evaluation of different algorithms is given. A new quantitative metric is proposed to evaluate the
visibility of targets in the detection results.
Keywords: target detection, mixture tuned matched filter (MTMF), mixture tuned target-constrained interferenceminimized
filter (MTTCIMF), minimum noise fraction (MNF), adaptive coherence estimator (ACE), orthogonal
subspace projection (OSP), constrained energy minimization (CEM), target visibility
In this paper, we present a fuzzy rule base system for object-based feature extraction and classification on remote sensing imagery. First, the object primitives are generated from the segmentation step. Object primitives are defined as individual regions with a set of attributes computed on the regions. The attributes computed include spectral, texture and shape measurements. Crisp rules are very intuitive to the users. They are usually represented as "GT (greater than)", "LT (less than)" and "IB (In Between)" with numerical values. The features can be manually generated by querying on the attributes using these crisp rules and monitoring the resulting selected object primitives. However, the attributes of different features are usually overlapping. The information is inexact and not suitable for traditional digital on/off decisions. Here a fuzzy rule base system is built to better model the uncertainty inherent in the data and vague human knowledge. Rather than representing attributes in linguistic terms like "Small", "Medium", "Large", we proposed a new method for automatic fuzzification of the traditional crisp concepts "GT", "LT" and "IB". Two sets of membership functions are defined to model those concepts. One is based on the piecewise linear functions, the other is based on S-type membership functions. A novel concept "fuzzy tolerance" is proposed to control the degree of fuzziness of each rule. The experimental results on classification and extracting features such as water, buildings, trees, fields and urban areas have shown that this newly designed fuzzy rule base system is intuitive and allows users to easily generate fuzzy rules.
Recently available commercial high-resolution satellite imaging sensors provide an important source for urban remote sensing applications. The high spatial image resolution reveals very fine details in urban areas and greatly facilitates the extraction of urban-related features such as roads, buildings, and vehicles. Since many urban land cover types have significant spectral overlap, structural information obtained using mathematical morphologic operators can provide complementary information to improve discrimination of different urban features. Here we present research demonstrating new applications of mathematical morphology for urban feature extraction from high-resolution satellite imagery. For image preprocessing, an alternating sequential filter is used to eliminate small spatial-scale disturbances to facilitate the extraction of larger-scale structures. For road extraction, directional morphological filtering is exploited to mask out those structures shorter than the distance of a typical city block. For building extraction, a recently introduced concept called the differential morphological profile (DMP) is used to generate building and shadow hypotheses. For vehicle detection, a morphological shared-weight neural network is used to classify image pixels on roads into target and non-target. Thus, mathematical morphology has a wide variety of useful applications for urban feature extraction from high-resolution satellite imagery.