Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Cảm nhận phân cấp không gian và học tập số liệu đối tượng khó cho phát hiện đối tượng trong hình ảnh viễn thám độ phân giải cao
Tóm tắt
Do các góc chụp, độ cao và cảnh sắc khác nhau, hình ảnh viễn thám chứa nhiều bối cảnh phức tạp và các đối tượng đa quy mô. Hơn nữa, các đối tượng trong hình ảnh viễn thám thường nhỏ hơn nhiều so với bối cảnh, dễ bị che khuất bởi các tòa nhà và cây cối. Điều này gây khó khăn trong việc trích xuất đặc trưng và làm tăng sự đa dạng trong cùng một lớp của các đối tượng, khiến cho việc phát hiện đối tượng trên hình ảnh viễn thám trở nên thách thức hơn. Trong bài báo này, chúng tôi đề xuất một phương pháp phát hiện đối tượng trong hình ảnh viễn thám mới (SHDet) dựa trên thành phần cảm nhận phân cấp không gian (SHPC) và học tập số liệu đối tượng khó (HSML). Chúng tôi thiết kế SHPC để trích xuất đặc trưng dưới các phân cấp không gian khác nhau và học trọng số đóng góp giữa các kênh đặc trưng nhằm tăng cường khả năng biểu diễn đặc trưng. HSML được đề xuất để thu hẹp sự khác biệt về đặc trưng của các mẫu khó trong cùng một loại, giảm thiểu sai số phát hiện do sự đa dạng trong cùng lớp. Bên cạnh đó, chúng tôi tách rời bối cảnh phức tạp để xây dựng các tập dữ liệu tiền huấn luyện cho việc tiền huấn luyện mô hình phát hiện đối tượng, củng cố việc học tập đặc trưng của đối tượng. Các thí nghiệm được tiến hành trên hai tập dữ liệu viễn thám được sử dụng rộng rãi (NWPU VHR-10 và DOTA-v1.5) cho thấy phương pháp đề xuất có hiệu suất phát hiện tốt hơn so với một số phương pháp phát hiện đối tượng tiên tiến nhất hiện nay.
Từ khóa
#viễn thám #phát hiện đối tượng #đặc trưng #phân cấp không gian #học số liệu khóTài liệu tham khảo
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