This paper presents a robust change detection algorithm for high-resolution panchromatic imagery using a proposed dual-dense convolutional network (DCN). In this work, a joint structure of two deep convolutional networks with dense connectivity in convolution layers is designed in order to accomplish change detection for satellite images acquired at different times. The proposed network model detects pixel-wise temporal change based on local characteristics by incorporating information from neighboring pixels. Dense connection in convolution layers is designed to reuse preceding feature maps by connecting them to all subsequent layers. Dual networks are incorporated by measuring the dissimilarity of two temporal images. In the proposed algorithm for change detection, a contrastive loss function is used in a learning stage by running over multiple pairs of samples. According to our evaluation, we found that the proposed framework achieves better detection performance than conventional algorithms, in area under the curve (AUC) of 0.97, percentage correct classification (PCC) of 99%, and Kappa of 69, on average.

This paper proposes a change detection algorithm on multi-spectral images based on feature-level U-Net. A low-complexity pan-sharpening method is proposed to employ not only panchromatic images, but also multi-spectral images for enhancing the performance of the deep neural network. The high-resolution multi-spectral (HRMS) images are then fed into the proposed feature-level U-Net. The proposed feature-level U-Net consists of two-stages: a feature-level subtracting network and U-Net. The feature-level subtracting network is used to extract dynamic difference images (DI) for the use of low-level and high-level features. By employing this network, the performance of change detection algorithms can be improved with a smaller number of layers for U-Net with a low computational complexity. Furthermore, the proposed algorithm detects small changes by taking benefits of both geometrical and spectral resolution enhancement and adopting an intensity-hue-saturation (IHS) pan-sharpening method. A modified of IHS pan-sharpening algorithm is introduced to solve spectral distortion problem by applying mean filtering in high frequency. We found that the proposed change detection on HRMS images gives a better performance compared to existing change detection algorithms by achieving an average F-1 score of 0.62, a percentage correct classification (PCC) of 98.78%, and a kappa of 61.60 for test datasets. INDEX TERMS Convolutional neural network, deep learning, remote sensing, satellite images, change detection.

This paper proposes a fusion network for detecting changes between two high-resolution panchromatic images. The proposed fusion network consists of front-and back-end neural network architectures to generate dual outputs for change detection. Two networks for change detection were applied to handle image-and high-level changes of information, respectively. The fusion network employs single-path and dual-path networks to accomplish low-level and high-level differential detection, respectively. Based on two dual outputs, a two-stage decision algorithm was proposed to efficiently yield the final change detection results. The dual outputs were incorporated into the two-stage decision by operating logical operations. The proposed algorithm was designed to incorporate not only dual network outputs but also neighboring information. In this paper, a new fused loss function was presented to estimate the errors and optimize the proposed network during the learning stage. Based on our experimental evaluation, the proposed method yields a better detection performance than conventional neural network algorithms, with an average area under the curve of 0.9709, percentage correct classification of 99%, and Kappa of 75 for many test datasets.

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The existence of a ready-to-eat meal with a long shelf life is essential, particularly for emergency food and military diet, as well as for alternative food for modern society. Rendang is a well-known Indonesian traditional food which may be packaged in a retort bag by employing a sterilization process. The sterilization value of Rendang is presently still limited. This study aimed to estimate sterilization value or heat sufficiency (F 0) using the General Method and the Ball Formula Method. The estimated value was then compared with the value obtained from the experiment. It was further used to predict process time (P t) to minimize the need for trial optimization on the product. The thermal sterilization process was carried out using a horizontal retort with a pressure of 1.3 bar for 40 minutes. The results showed that the F 0 values predicted were in a close agreement with the observed (experimental) values. F 0 values were 5.88, 5.17, and 5.91 minutes for the General Method, Formula Method, and the observation, respectively. The P t values of Beef Rendang in a retort bag with F 0 values of 4, 5, 8, 10, and 12 minutes were 25, 27, 31, 34, and 36 minutes for retort temperature of 123°C, respectively. Whereas for a retort temperature of 121°C, the P t values were 28, 30, 36, 39, and 42 minutes, respectively. In conclusion, both the General Method and Ball Formula are accurate to estimate the sterilization value of Beef Rendang packaged in Retort Pouch.