Phân tích đặc trưng dựa trên đối tượng cho dữ liệu siêu phổ sử dụng thuật toán đom đóm

Hamid Reza Shahdoosti1, Zahra Tabatabaei1
1Department of Electrical Engineering, Hamedan University of Technology, Hamedan, Iran

Tóm tắt

Các phương pháp phân loại dựa trên đối tượng có thể cải thiện độ chính xác của phân loại hình ảnh siêu phổ nhờ vào việc tích hợp thông tin không gian vào quy trình phân loại bằng cách gán các pixel lân cận vào cùng một lớp. Trong bài báo này, một phương pháp trích xuất đặc trưng dựa trên đối tượng mới được đề xuất, sử dụng lý thuyết thông tin để giảm sai số Bayes. Bằng cách này, phương pháp đề xuất khai thác các thống kê bậc cao cho việc trích xuất đặc trưng, điều này rất hiệu quả cho dữ liệu không Gaussian như hình ảnh siêu phổ. Tiêu chí cần tối thiểu hóa bao gồm ba thành phần thông tin tương hỗ. Hai thành phần đầu tiên xem xét tính liên quan tối đa và tính dư thừa tối thiểu, trong khi thành phần thứ ba xem xét bản đồ phân đoạn chứa các miền không giao nhau. Để có được bản đồ phân đoạn, chúng tôi áp dụng thuật toán phân cụm đom đóm, mà trong hàm thích nghi của nó đồng thời xem xét khoảng cách trong giữa các mẫu và tâm cụm của chúng, cũng như khoảng cách giữa tâm của hai cụm bất kỳ. Kết quả thực nghiệm của chúng tôi, được thực hiện trên nhiều cảnh siêu phổ khác nhau, cho thấy rằng khung phương pháp đề xuất cho kết quả phân loại tốt hơn so với một số phương pháp trích xuất đặc trưng quang phổ-không gian tiên tiến.

Từ khóa

#Phân loại dựa trên đối tượng #Trích xuất đặc trưng #Hình ảnh siêu phổ #Lý thuyết thông tin #Thuật toán đom đóm

Tài liệu tham khảo

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