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In view that traditional manual feature extraction method cannot effectively extract the overall deep image information, a new method of scene classification based on deep learning feature fusion is proposed for remote sensing images. First, the Grey Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) are used to extract the shallow information of texture features with relevant spatial characteristics and local texture features as well; second, the deep information of images is extracted by the AlexNet migration learning network, and a 256-dimensional fully connected layer is added as feature output while the last fully connected layer is removed; and the two features are adaptively integrated, then the remote sensing images are classified and identified by the Grid Search optimized Support Vector Machine (GS-SVM).The experimental results on 21 types of target data of the public dataset UC Merced and 7 types of target data of RSSCN7 produced average accuracy rates of 94.77% and 93.79%,respectively, showing that the proposed method can effectively improve the classification accuracy of remote sensing image scenes. [ABSTRACT FROM AUTHOR]

The vital behaviors of Magang geese are closely related to their growth and welfare status. Therefore, it is very necessary to accurately identify the key vital behaviors of Magang geese in husbandry and production. This study aims to efficiently and accurately monitor the key behaviors of Magang geese in pens. An improved algorithm was also proposed for the vital behavior of Magang geese using Double Head-YoloX (referred to as MGBM-DH-YoloX). The detection efficiency of the network was enhanced to reduce the number of YoloX heads. A tradeoff was made to balance the foreground and background using the Focal loss objective function. Efficient network training was achieved using migration training. Other techniques were utilized to identify the vital behavior and count their key behaviors once at a fixed time in an efficient and accurate way, including the identification and rule analysis of key behaviors, such as feeding, drinking, rest and stress of Magang Geese. Firstly, MGBM-DH-YoloX enhanced the data of the Magang geese images with Mosaic and Mixup. And then the enhanced dataset was used to train the DH-YoloX (Double Head - YoloX) for the detection of the vital behavior of the waterfowl. Finally, the vital behavior of the Magang geese was counted every 25 frames. The experiment was conducted in a flock-reared environment with the Magang geese as the subject of the case study. A behavioral target detection test was conducted during this time. Waterfowl videos were collected on multiple days from 26 December 2021 to 04 January 2022, and from 10-12 May 2022 at the Nanwei Building Experimental Farm, School of Animal Science, South China Agricultural University, Tianhe District, Guangzhou City, Guangdong Province, China. A platform was also built to acquire the data for the behavior detection of geese. The color images of Magang geese were facilitated to integrate the environmental factors in the vicinity of the breeding pen (the height of the pen, as well as the width and height of the equipment box). The bi-directional difference frame was adopted to extract the key frames, in order to quickly extract the required picture production dataset from the video data. A total of 1 600 picture data was extracted from the video data, and selected for a total of 2 000 picture data. A total of 1 600 images were randomly used for the training and validation sets, whereas, 400 images were for the test set in the 10 days of continuous activity video. The results showed that the MGBM-DH-YoloX algorithm achieved an average accuracy of 98.98% mAP, a processing frame rate of 81 frame/s, and a memory consumption of 2 520.04 MB for the detection of Magang geese’s behaviors. Meanwhile, it was found that the geese were foraged less frequently, as they grow older, after monitoring the 10-day breeding data. The vital foraging and drinking behaviors simultaneously accounted for 83.74% of the total foraging time per day, indicating the overall companionship trend. There was also a decrease from 90.78% to 74.57%, indicating the gradual separation from the feeding and drinking behavior of Magang Geese with the increase of age. On the contrary, the resting behavior of the experimental geese increased slowly with the increase of age, indicating a gradual adaptation to the caging. The stress behavior was highly random in this experiment. There was an extremely serious emergency caused by irregular feeding, such as the random walking of personnel. Consequently, the MGBM-DH-YoloX can be expected as the video monitoring to intelligently extract the vital behavior of Magang geese. The finding can provide technical support for the automated monitoring of poultry in intelligent breeding supervision. [ABSTRACT FROM AUTHOR]

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In order to solve the problem in extracting features manually in the application of remote sensing, which is time-consuming, labor-intensive and low accuracy, and is hard to acquire the annotated data, the tree species classification algorithm of convolutional neural network remote sensing image based on migration learning is proposed. Firstly, the lneven-v3 model trained on ImageNet is introduced to extract the image features of tree species. A new fully connected layer and Softmax layer are trained to change the figure of nodes in the output layer through the AID standard dataset and the self-collecting dataset of experimental forest farm of Northeast Forestry University. Secondly, the phenomenon of overfitting is improved through the introduction of the Dropout function. Finally, the model is optimized by back propagation. The experiment results show that the overall classification accuracies on two data sets are 98. 8% and 97. 2%, respectively, and the Kappa coefficients are 0. 987 and 0. 984, respectively. The experiment results indicate that the algorithm mentioned above can not only reduce the number of parameters of the convolutional neural network, but also improve the accuracy of target classification and recognition. Compared with the traditional methods of tree species classification, this algorithm can automatically extract the characteristics of tree species, and the problem that the high accuracy depends on the number of experimental samples is solved, and this algorithm has high application value in tree species classification. [ABSTRACT FROM AUTHOR]