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With the continuous development of hyperspectral remote sensing technology, it has been widely used in vegetation mapping. However, sparse vegetation canopy, soil background, and spectral similarity between different types of vegetation are still the main challenges for vegetation types mapping in arid areas. As a result, it is difficult to achieve reliable classification accuracy by using spectral or texture features separately. Generalized Normal Distribution Optimization (GNDO) is a new feature optimization algorithm, with advantages in quality and stability of feature extraction results, comparing to traditional optimization algorithms. But it has not yet been applied to select bands of hyperspectral data. In order to validate the feasibility of combining ZY-1 02D spectral and texture features to classify vegetation types in arid areas, to verify the effectiveness of the GNDO method for bands selection of hyperspectral data, and to explore the effects of feature selection methods and training sample numbers on the classification accuracy of vegetation mapping, different Wrapper Optimization methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and GNDO, were applied to select spectral features for vegetation mapping, taking the area around Zongjia Town, Dulan County, Qinghai Province, China as the research area, then the band selection results of these methods were analyzed. Train sample set containing 30, 50, 100, 150, and 200 pixels per class were used to select bands and to train the classifier. Different methods (ALL (without bands selection), GA, PSO, GWO, GNDO) and different sizes of the trained sample sets (30, 50, 100, 150, and 200 pixels per class) were used to obtain 25 spectral feature-based classification data sets. Simultaneously, 8 texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment) were extracted using the Gray-level co-occurrence matrix (GLCM) method, and selected on basis of distinguishability for vegetation types. Texture features (TEX) were combined with spectral feature-based classification data sets. The random forest classification method was applied to classify vegetation types for the classification data sets, and the classification accuracy of classification data sets was evaluated and compared. The results show that 1) the blue region (400-450 nm), the red edge region (700-750 nm), and the red region (600-650 nm) are the most sensitive to distinguish the vegetation types in the study area; 2) the GNDO200 achieved the highest overall classification accuracy (80.44%) among the spectral feature-based classification data sets, which was better than the classification accuracy (78.86%) using all bands (ALL200); 3) with the increase of training samples, the overall classification accuracy of each classification data set showed an increasing trend, the classification accuracy of different feature selection methods showed different reliance on the number of training samples; 4) image texture features significantly improved the classification accuracy, and the GWO200+TEX dataset had the highest overall classification accuracy (82.86%). This study could verify the potential of the ZY1-02D, the new hyperspectral satellite data, for the classification of vegetation types in arid areas, and provide an idea for the selection of spectral and texture features in hyperspectral vegetation mapping. [ABSTRACT FROM AUTHOR]

A recirculating aquaculture system (RAS) has widely been acknowledged as eco-friendly land-based aquaculture since only a limited amount of water is used in recirculation. There is also much greater potential in modern intensive aquaculture, compared with conventional fish production. A key feature of RAS is the high precise controlling of the water environment in the rearing tank and the feeding of rearing fish. Water environmental parameters (such as water temperature, and oxygen levels) can provide stable and optimal conditions for less stress and better growth during fish living. In addition, fish feeding plays a vital role in RAS, because starvation may impact the organizational structures of fish, whereas, overfeeding may threaten the survival of animals, as well as the water quality. Therefore, it is essential to investigate the strategy of fish feeding in RAS. Currently, the feeding events are mainly implemented by the feeding machine with predetermined and fixed feeding times and quantities, according to the experience rather than the real fish appetite. Thus, it is necessary to evaluate the fish behavior pattern and appetite in the feeding strategy for a higher performance of RAS. In this study, an innovative evaluation of fish feeding behavior was proposed using Fourier spectrum feature extraction and support vector machines (SVM) classification. The degree of surface ripple dispersion was considered to predict the fish feeding behavior, rather than the only movement of fish bodies in conventional cases. Firstly, a high-speed video camera was used to capture the process before and after feeding. The video images were then converted from RGB to HSV space, where the background including the water ripple was subtracted, and the contrast was enhanced via the thresholds of saturation and brightness. Secondly, fast Fourier transform (FFT) was adopted to transform the image information from the spatial to a frequency domain. The fluctuation level of ripples in the image was regarded as an indicator of fish appetite using the amplitudes in the frequency spectrum. Specifically, the frequency spectrum was characterized by a high frequency, short period, and a large sum of amplitudes, when the fishes presented high appetite, and vice versa. Thirdly, the database of feature vectors was generated using the self-developed loop filters with various diameters. Finally, the obtained feature vector was trained to SVM. The feature vector of the SVM model was then optimized, where the optimal kernel width and penalty coefficient were selected to efficiently judge the feeding desire of fish during the cross validation. Moreover, a comparison was conducted to verify the efficiency, among the proposed, optical flow, image texture, shape, and texture features. As such, the original interference condition was transformed into the judgment, indicating the better universality of RAS against the high fish density, unstable water surface ripple, and dark light in the system. A field test also demonstrated that a high accuracy of 99.24% was achieved in judging the feeding behavior of farmed fish in RAS. In practice, it is strongly recommended to take videos of farmed fish as the training data for the classifier of fish feeding behavior. Consequently, the improved RAS can also be incorporated into the feeding machine to supervise the real-time smart and precise feeding events in modern aquaculture. [ABSTRACT FROM AUTHOR]

An intelligent fruit and vegetable recognition system utilizing image recognition can accurately and rapidly indentify different kinds of fruits and vegetables, which can improve supermarket and market sales efficiency. The feature extracting method is a very important issue in an intelligent fruit and vegetable recognition system. However, traditional fruit and vegetable recognition algorithms either ignore the texture feature of fruits and vegetables, or used texture features that couldn't better represent the texture of fruit and vegetable images. In order to represent the texture feature of fruit and vegetable images better and improve the intelligent fruit and vegetable recognition system recognition rate, we proposed a novel texture feature extraction algorithms called color completed local binary pattern (CCLBP) in this paper. By extracting different kinds of color channels completed by a local binary pattern (CLBP) texture feature, the CCLBP constructed a new texture feature extraction algorithm. The Fruit and vegetable recognition system model uses CCLBP to extract an image texture feature, and uses a HSV color histogram and Border/interior pixel classification (BIC) color histogram to extract image color features. Then it uses a matching score fusion algorithm to fuse color and texture features, and finally, a nearest neighbor (NN) classifier is used to realize fruit and vegetable recognition. To verify the effectiveness of the algorithms, two different fruit and vegetable databases, called an interior database and an outdoor database, were constructed in this paper. The interior database acquired in a laboratory contains 13 kinds of fruits and vegetables, which is used to verify algorithms recognition performance under different kinds of illumination. The outdoor database acquired in the market contains 47 kinds of fruits and vegetables, which is used to verify algorithms recognition performance under a different number of training sets. A Fruit and vegetable recognition experiment under different kinds of illumination showed that, only by the texture feature indentifying the kinds of fruits and vegetables, the recognition rate of the CCLBP was 5% higher than the traditional fruit and vegetable texture features (such as Unser, TestA), which means that the CCLBP is more suitable for fruit and vegetable recognition; besides, compared with other texture algorithms, the CCLBP fused with HSV color histogram and BIC color histogram can achieve a 73.93% highest mean recognition rate, which takes about 1.1 seconds indentifying an image. A fruit and vegetable recognition experiment under a different number of training sets had similar results as the experiment under different kinds of illumination. The recognition rate of the CCLBP was still higher than the traditional fruits and vegetables texture features. What's more, the CCLBP fused with a HSV color histogram and a BIC color histogram can achieve 94.26%, the highest recognition rate. The experiments under different kinds of illumination and under different number of fruits and vegetables confirm the feasibility of our algorithm. Our algorithm can be used in intelligent fruit and vegetable recognition system, which improves the system accuracy rate. [ABSTRACT FROM AUTHOR]

In order to solve the problem of muskmelon fruit image segmentation under a complex background, an algorithm of image segmentation based on fusing color feature and texture feature was proposed in this paper. First, the collected muskmelon fruit images were transformed from RGB color space to CIELAB color space and HSV space respectively. According to the color characteristics of muskmelon fruit, the collected images were binarized using the threshold of angle model that was set up in using a*b* components in CIELAB color space. To reduce the influence of the uneven illumination distribution of segmentation, the H S components segmentation threshold was selected to binarize the collected images. Converging the results of the angle model segmentation and the HS weighted threshold segmentation, the results were obtained based on color feature segmentation. Then, the image texture features were extracted and the binarization images were obtained by using texture feature threshold. The segmentation results were achieved by fusing the texture features and the color feature segmentation result. Finally, taking the fruit color feature segmentation area as the qualification, the final segmentation results were obtained by binding growth based on the segmentation area that were obtained by fusing color features and texture features. In order to evaluate the effect of proposed algorithm, the collected images were segmented using the super green threshold algorithm and the NDI algorithm and the results were gained. The average detection rate of three algorithms were 83.24%, 43.12% and 99.09%, respectively. Comparing the results of the detection rate and false detection rate, the experimental results of the proposed algorithm were superior to super green feature segmentation and normalized difference index (NDI) segmentation algorithm. [ABSTRACT FROM AUTHOR]