A New Approximate Sum of Absolute Differences Unit for Bioimages Processing

2chADCNN: A Template Matching Network for Season-Changing UAV Aerial Images and Satellite Imagery

“…We compared the 2chADCNN method with three established datasets: synthetic winter snowy UAV aerial images-real spring satellite imagery (Syn-Winter), real summer UAV aerial images-real spring satellite imagery (Summer), and real winter snowy UAV aerial imagesreal spring satellite imagery (Winter), respectively, against SIFT [19], SSD [16], SAD [15], NCC [17], SuperGlue [37], LoFTR [38], MatchNet [26], and 2chDCNN [27] matching algorithms. We present some results as examples, as shown in Figures 9-11 From Figures 9-11, it can be seen that the prediction area of the matching results of the method in this paper was closer to the real area for the matching results of UAV aerial images and satellite images in different seasons.…”

“…Area-based methods [13,14] typically use a small template image and compare its similarity with a large target image, to search for the most similar position. The traditional area-based matching methods based on gray information usually employ pixel-by-pixel comparisons, such as SAD (sum-ofabsolute-differences) [15], SSD (sum-of-squared-differences) [16], and NCC (normalizedcross-correlation) [17]. These methods have the advantages of simple operation and fast calculation; however, the accuracy is significantly reduced when image distortion, different lighting, and different sensors are present.…”

“…SIFT [19,29] 0.218 0.642 0.174 SSD [16] 0.207 0.681 0.162 SAD [15] 0.258 0.670 0.192 NCC [17] 0.745 0.855 0.417 SuperGlue [37] 0.758 0.889 0.516 LoFTR [38] 0.822 0.787 0.631 MatchNet [26] 0.362 0.430 0.276 RORD [52] 0.835 0.815 0.593 LightGlue [53] 0.591 0.820 0.284 2chDCNN [27] 0.743 0.871 0.532 The Proposed Method 0.839 0.940 0.749…”

“…SIFT [19] 0.22 0.64 0.17 0.0069 SSD [16] 0.21 0.68 0.17 0.0019 SAD [15] 0.26 0.66 0.19 0.0020 NCC [17] 0.74 0.86 0.43 0.0021 SuperGlue [37] 0.76 0.89 0.51 0.1101 LoFTR [38] 0.82 0.79 0.63 0.0337 MatchNet [26] 0.37 0.43 0.28 0.0299 RORD [52] 0.84 0.82 0.60 0.1217 LightGlue [53] 0.59 0.82 0.28 0.3061 2chDCNN [27] 0 It can be observed from Table 2 that all algorithms exhibited excellent performance in matching real summer UAV aerial images without snow cover and real spring satellite imagery. However, for real and synthetic winter snowy images, the other algorithms performed poorly, confirming that snow cover significantly affects feature matching in UAV aerial images.…”