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This study proposes a new water body classification method using top-of-atmosphere (TOA) reflectance and water indices (WIs) of the Landsat 8 Operational Land Imager (OLI) sensor and its corresponding random forest classifiers. In this study, multispectral images from the OLI sensor are represented as TOA reflectance and WI values because a classification result using two measures is better than raw spectral images. Two types of boosted random forest (BRF) classifiers are learned using TOA reflectance and WI values, respectively, instead of the heuristic threshold or unsupervised methods. The final probability is summed linearly using the probabilities of two different BRFs to classify image pixels to water class. This study first demonstrates that the Landsat 8 OLI sensor has higher classification rate because it provides improved signal-to-ratio radiometric by using 12-bit quantization of the data instead of 8-bit as available from other sensors. In addition, we prove that the performance of the proposed combination of two BRF classifiers shows robust water body classification results, regardless of topology, river properties, and background environment.

We propose a novel object-of-interest (OOI) segmentation algorithm for various images that is based on human attention and semantic region clustering. As object-based image segmentation is beyond current computer vision techniques, the proposed method segments an image into regions, which are then merged as a semantic object. At the same time, an attention window (AW) is created based on the saliency map and saliency points from an image. Within the AW, a support vector machine is used to select the salient regions, which are then clustered into the OOI using the proposed region merging. Unlike other algorithms, the proposed method allows multiple OOIs to be segmented according to the saliency map.

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This paper presents a fast and efficient method for classifying X-ray images using random forests with proposed local wavelet-based local binary pattern (LBP) to improve image classification performance and reduce training and testing time. Most studies on local binary patterns and its modifications, including centre symmetric LBP (CS-LBP), focus on using image pixels as descriptors. To classify X-ray images, we first extract local wavelet-based CS-LBP (WCS-LBP) descriptors from local parts of the images to describe the wavelet-based texture characteristic. Then we apply the extracted feature vector to decision trees to construct random forests, which are an ensemble of random decision trees. Using the random forests with local WCS-LBP, we classified one test image into the category having the maximum posterior probability. Compared with other feature descriptors and classifiers, the proposed method shows both improved performance and faster processing time.